-
Hiroki Takahashi
This course introduces basic notions of quantum optics and prepares a theoretical foundation that facilitates understanding the working principles of modern quantum devices, such as linear optical quantum computers, ion traps, superconducting circuits etc. In many cases physical systems used in quantum technology applications can be described by simple quantum physics of spins (two level systems) and harmonic oscillators. We start from basic algebras of a, a^dagger and Pauli, and then move on to topics such as coherent states, squeezed states, (anti-)bunching, photon statistics, Rabi oscillation, Bloch sphere, Ramsey interference, cavity QED, master equations, quantum input-output relation, two-qubit entangling gate, ion traps, Josephson junctions, circuit QED.
Target students
Students who are or will be working on applied quantum physics.
Students who wish to understand the working principles of quantum technologies.
Prior knowledge
Basic knowledge about
・Undergrad-level quantum mechanics
・Undergrad-level linear algebra
3
Term
2
Credits
-
Xiaodan Zhou
The concept of a measure is a natural generalization of length, area, volume and other common notions, such as mass and probability of events. These seemingly distinct concepts have many similarities and can often be treated together in a single mathematical context. Measures are foundational in probability theory, integration theory, and can be generalized to be widely used beyond mathematics.
Though the concept of measure dates to ancient Greece, it was not until the late 19th and early 20th centuries that measure theory became a branch of mathematics. In particular, the work by French mathematician Henri Lebesgue plays a foundational role in modern measure theory. Lebesgue’s formulation of measure easily extends to quite an abstract setting and forms a basic language of probability theory and Lebesgue integral is robust under various limiting operations, overcoming the drawbacks of Riemann integrals. This is important, for instance, in the study of Fourier series, Fourier transforms, and other topics
The study of measure and integration lays the foundation for more advanced mathematical topics including functional analysis, partial differential equations, Fourier analysis, etc. In this course, we will visit many fundamental concepts of Lebesgue measure and integration theory through the lecture and exercise.
Prior knowledge
B36 “Introduction to Real Analysis” is recommended but not required. The following is expected prerequisite knowledge: basic set theory, mathematical logic, the fundamental property of real numbers; familiarity with limit definitions, and how to use these definitions in rigorous proofs of sequences, continuity and differentiation of real-valued functions; properties of a supremum (or least upper bound) and infimum (or greatest lower bound); basic topology including the definitions of open, closed, compact sets in the Euclidean space; basic definitions and properties of Riemann integrals. Please contact the instructor at the beginning of the course with questions.
1
Term
0
Credits
-
Vincent Laudet
Because they live in water, an environment which is completely foreign to us, but also because of their distance from us, their cold blood; their bodies covered with scales and their absence of expression, we often consider fish as dumb creatures of little interest, just good to be grilled.
And yet! What diversity! What incredible abilities! What fantastic adaptations! Fish have conquered virtually all the aquatic environments of our planet, even the most extreme. They are capable of elaborate behaviors and of complex social interactions and they can express individual characters like shyness or boldness. Like birds, they communicate with elaborate sounds. We have too largely ignored these fascinating animals because we misunderstand them and their environment. This course will give you a much better and interesting vision of these animals and will also show you how a biological problem can benefit of being studied by different scientific approaches.
Formerly listed as A317. Student can receive credit for only A 317 or B42.
1
Term
2
Credits
-
Christine Luscombe
Polymers are ubiquitous and are used in everything from clothing, plastics, paints, and adhesives. They are also used in biological applications including drug delivery and tissue engineering, and many nature’s macromolecules are polymers. This course will cover the synthesis of polymers, as well the interrelationship between molecular structure, morphology, and properties. Applications of polymers will be discussed and the course will end with a discussion on the environmental impact of polymers.
Target students
This course is intended for those interested in learning how the most ubiquitous material, out of all materials classes, function. The course does not require prior knowledge of polymers but it is intended that we will end with graduate level understanding of polymers. The course will cover a wide range of applications including using polymers for electronics and biological applications and thus should be suitable for a wide range of students.
Prior knowledge
None
1
Term
2
Credits
-
David Armitage
The field of ecology is guided by one central question: What are the processes that determine the distribution and abundance of organisms? This course will introduce you to the fundamental theory and problems in ecology through reading, discussion, and lecture. Special attention will be paid to the principles governing population dynamics over time and space, theories of community assembly and species coexistence, and processes of material cycling through ecosystems. Beyond the specific subject matter, training in ecology can prepare one’s mind to appreciate the causal feedbacks, scale dependencies, and contingencies of the complex social world we inhabit.
Prior knowledge
Undergraduate-level coursework in general biology and calculus are recommended but not required.
1
Term
2
Credits
-
Denis Konstantinov
This course is complementary to OIST courses B12 Statistical Physics and A225 Statistical Mechanics, Critical Phenomena and Renormalization Group. The course covers a few important topics from Statistical Physics, particularly, description of systems away from (but close to) the thermal equilibrium.
First, we discuss fluctuations of statistical quantities in a system, mostly at thermal equilibrium, and derive the very important relation (FDT) between fluctuations and dissipation in a dynamic system coupled to a noisy environment. Next, we attempt to provide a description for systems driven out of equilibrium by external forces and derive methods to account for transport of various physical quantities, such as particle’s number, momentum and thermal energy. Finally, we try to extend some of the above ideas to quantum systems, in particular those interacting with an environment, aiming to give a very basic introduction to the theory of dissipative (“open”) quantum systems.
During the course, we aim at developing a good (sometimes intuitive) understating of the physical picture rather than pursuing a rigorous mathematical description of the phenomena. Numerous examples and model problems from solid state and condensed matter physics, atomic physics, quantum optics, etc., will be discussed to illustrate the methods.
Prior knowledge
Statistical Physics (B12) or Statistical Mechanics, Critical Phenomena and Renormalization Group (A225); anything equivalent to a basic course on Nonrelativistic Quantum Mechanics
1
Term
2
Credits
-
Mohammad Emtiyaz Khan
Machine learning aims to design machines that can autonomously learn like humans and animals. In this course, basic principles and methods of machine learning will be introduced. You will learn a few basic methods, how they relate to each other, and why they work.
Target Students
This course is intended for beginners in machine learning with some background in basic math and programming.
Course Materials
1) T. Hastie, R. Tibshirani and J. Friedman: Elements of statistical learning, free download from http://statweb.stanford.edu/~tibs/ElemStatLearn/
2) C. Bishop: Pattern Recognition and Machine Learning (available online)
Assessment items
1. [40%] Class summary: Students will summarize every two weeks of lecture in their own words (a total of 5 such reports). This needs to be a summary based on understanding and can be as short as as 2 pages.
2. [40%] Project report and presentation: students will submit a final project report in Week 13, and present their work in Week 14. The grading will be based on constructive feedback from the class on the project and presentation.
3. [20%] Class discussions Grading expectations
Project Description: Students will work on a project in a team of 2 people, and do the following:
- Implement a method from scratch
- Evaluate its performance and compare to a simple baseline
- Write up findings in a 4-page report
- Give a 15 minute oral-presentation about it
Prior knowledge
• Programming in at least 1 language, such as Python/Matlab/R (basic level)
• Basic linear algebra (system of linear equations and SVD)
• Basic multivariate calculus (deriving gradients)
• Basic probability and statistics (conditional and joint distribution, independence, Bayes rule, random variables, expectation, mean, median, mode, central limit theorem)
• Univariate and multivariate Gaussian distribution (joint, conditional, and marginals)
It is a plus if you have taken one of either of these courses (but not a requirement):
• Jun Tani, Cognitive Neurorobotics, https://groups.oist.jp/course/course-neurorobotics
• Tomoki Fukai, Statistical Modeling, https://groups.oist.jp/course/statistical-modeling
3
Term
2
Credits
-
Gail Tripp
This course is designed to introduce students to research with human subjects. The course will cover study conceptualization, research design, sampling and data collection methods, ethics, and statistical treatment of data. The emphasis will be on behavioral sciences research, but the content will generalize across study fields.
Target Students
The course will be of interest to those undertaking human subjects research or working with human subjects’ data. Those interested in extending their understanding of clinical and behavioral sciences research will also find the course valuable.
Course Materials
Readings will be assigned. This will include original research articles, research methods papers, and sections of relevant research methods textbooks. The latter will be provided by the faculty member teaching the course. In addition, students will be expected to undertake their literature searches to identify resources for course assignments.
Assessment items
This course will be fully (100%) internally assessed. Both class participation and assignment completion will contribute to the final grade.
1. Class participation (30% of grade)
Completion of weekly readings; responses to weekly thought questions; active participation in class discussions.
Given the importance of active class participation, attendance is expected.
2. Preparation of an information letter(s) and consent form(s) for a human subjects’ research study, (20% of grade)
For this assignment students may prepare materials for their own research (if relevant) or select a research paper describing a study with human participants and prepare the information letter(s) and consent form(s) for that study. Students cannot copy/use existing letters/consent forms in completing this assignment.
3. Class presentation, a 20-30 minute presentation (20% of grade).
In consultation with the faculty member teaching the class, students will choose a topic (from amongst the course content) to prepare and present to their classmates.
4. Preparation of a grant application/research proposa (background, hypotheses, methods, statistical analyses, significance) for a human subjects’ research study (30% of grade).
Students will identify a research question of interest, design a study to address the question and prepare a research proposal/grant application for the study. The faculty member teaching the class will be available to assist students in identifying research topic/question.
Prior knowledge
There are no prerequisites for this course. Students will be expected to complete assigned readings ahead of class in order to participate fully.
3
Term
2
Credits
-
Tom Froese
A student-centered introduction to the recent developments in embodied cognitive science. The course will be interactive with half of the classes focused on group-based discussions. These will permit students to collaboratively articulate the key issues for a broad range of themes. They are expected to reflect on these discussions and additional supplied reading material by formulating a question, which will then become the focus of a follow-up lecture dedicated to that theme. Students will be required to write an essay as part of this course, which will give them an opportunity to reflect upon how the latest ideas about how the mind works might relate to their own research.
Prior knowledge
Due to the highly interactive and group-based nature of the course, the number of students is limited to 9 and preference will be given to students with a background in one of the disciplines that form the cognitive sciences.
1
Term
2
Credits
-
Philipp Höhn
The course will broadly lie at the interface of condensed matter physics, quantum information theory and high-energy physics. The aim is to study correlation structures in quantum many-body systems and understand their role in determining the physical properties of these systems. Owing to the complexity of many-body systems, exploring typical quantum information concepts in them will require us to invoke efficient approximation and renormalization techniques. This will lead us to introduce tensor networks and the multi-scale entanglement renormalization ansatz, which are standard workhorses in the modern literature. We will pay special attention to ground states and their entanglement properties, such as entanglement entropy area laws and how correlations decay over distance. Quantum correlations are also intertwined with the spreading of information and we shall examine this topic in the form of Lieb-Robinson bounds. A further topic we will investigate is how (gauge) symmetries affect the correlation structure and computation of entropies. While most of the discussion will focus on finite-dimensional many-body systems, we will proceed to studying some of these questions in quantum field theory towards the end of the course.
Prior knowledge
Quantum Mechanics I and II and ideally Advanced Quantum Theory. A further background in Quantum Field Theory and Statistical Physics is helpful.
2
Term
2
Credits
-
Xiaodan Zhou
In most Calculus courses, we learned many useful computation techniques without further explanation of the concepts behind the tools. Real analysis, which sometimes can be roughly understood as “advanced calculus”, is to set the solid mathematical foundation for calculus. In this course, we will visit many fundamental concepts of mathematical analysis through the lecture and exercises.
The principal topics of the course include fundamentals of logic, basic set theory, functions, number systems, order completeness of the real numbers and its consequences, sequences and series, topology of R^n, continuous functions, uniform convergence, compactness, theory of differentiation and integration.
Target students and prerequisites:
The course is an introductory course and is designed to be accessible to students that are seeing proofs for the first time. The only prerequisite is an understanding of the results from single-variable calculus. Successful completion of undergraduate Calculus or equivalent courses is required to take this course. Multivariable calculus is not a prerequisite. If you are not sure about the prerequisite material, please contact the instructor at the beginning of the course.
Prior knowledge
Successful completion of undergraduate single-variable calculus
1
Term
2
Credits
-
Jason Twamley
Target Students
This course is targeted to students with a background in physics or any relevant discipline who have good knowledge of quantum mechanics and who wish to develop skills in the computational modelling of quantum machines. Advanced experience with Python is not required but some familiarity and competence with Python (such as taking the Python boot-camp) is required.
Description
This course explores the topic of integrated quantum devices. Such devices bring together different types of quantum systems which provide new functionality not possible within an individual quantum system. Such devices are used for fundamental quantum mechanic studies, quantum sensing, quantum communication and quantum computing. During the course students will develop “engineering” type skills towards learning how to design and model – theoretically and computationally, various types of composite quantum devices. Systems to be studied include integrated photonic with atomic, condensed matter and motional atomic systems including cavity quantum electrodynamics, cavity optomechanics, Nitrogen vacancy defects in diamond and levitated quantum systems.
The course consists of an initial section consisting of weekly lectures and computer labs. These labs are the central component of the course and during these labs students will learn computational techniques to study the properties of integrated quantum devices using python. The second section of the course consists of journal clubs and a final computational project with a poster presentation.
Prior knowledge
Undergraduate quantum mechanics (full year), experience is pre-required. This includes good knowledge of the quantum matrix mechanics for spin, Schrodinger equation (stationary and time dependent), and the operator treatment of the quantum harmonic oscillator including creation and annihilation operators. Desirable pre-knowledge includes cavity quantum electrodynamics and atoms interacting with electromagnetic radiation. Python boot-camp is required but no other Python skills are required.
3
Term
2
Credits
-
Akimitsu Narita
Target Students
Students with background of organic chemistry who are interested in the synthesis of carbon nanomaterials.
Description
This course mainly covers the synthetic methods for carbon nanomaterials, including fullerene derivatives, polyphenylenes, nanographenes, and graphene nanoribbons, from the perspectives of organic chemistry. Particular focus will be given on the synthesis of large polycyclic aromatic hydrocarbons (PAHs) developed over the past 100 years in the field of organic chemistry, which led to the recent bottom-up synthesis of atomically precise nanographenes and graphene nanoribbons. This course will also cover the related methods in polymer chemistry as well as the on-surface synthesis of nanocarbons, using the techniques of the surface science. Besides the synthetic methods, various spectroscopic and microscopic characterization methods available for the resulting carbon nanomaterials will be explained and their potential applications will be discussed.
Prior knowledge
Undergraduate-level knowledge of general chemistry; Advanced-level knowledge of organic chemistry.
3
Term
2
Credits
-
Filip Husnik
Target Students
The students should have a background in biology and biochemistry and be interested in learning more about single-celled organisms. Apart from students with background in evolutionary biology, molecular cell biology, ecology, marine biology, microbiology, biochemistry, developmental biology, etc., the course can be taken also by out-of-field students who would like to get a glimpse of evolution of life and microbial diversity.
Description
Most of the genetic, cellular, and biochemical diversity of life rests within single-celled organisms, prokaryotes (bacteria and archaea) and microbial eukaryotes (protists). Bacteria and archaea not only account for over 3.5 billion years of evolution, but also played a crucial role in the origin of the first eukaryotic-like (protist) cells approximately two billion years ago. However, most of our knowledge about evolution and cell biology (and how we frame it) comes from a small subset of eukaryotic diversity -- multicellular animals and plants. During the course, we will take a broad view of the immense diversity of single-celled organisms (both prokaryotes and eukaryotes), focusing on their evolution, ecology, genetics, biochemistry, and cell biology. We will explore their evolutionary history and highlight major cellular innovations that occurred in single-celled organisms during the evolution of life.
The successful student will be able to describe differences in evolution and cell biology of single-celled organisms as opposed to multicellular organisms. The course is designed partly to fix biases that students often acquire from working with ‘model organisms’ that are mostly multicellular (animals and plants) and partly to showcase the immense diversity of microorganisms. It is thus not a traditional microbiology course, but it rather focuses on selected broadly interesting aspects of microbial evolution and cell biology such as major evolutionary transitions and cellular innovations. The students should gain knowledge about the evolutionary ‘baggage’ from our single-celled history that constrains the functioning of any modern cell, and be able to apply the knowledge in their own projects.
Prior knowledge
Basic understanding of evolutionary and cell biology at the undergraduate level is assumed. The following courses offered at OIST are recommended to students who first want to review their knowledge: Molecular Biology of the Cell (B27) and Evolution (B37) [or Molecular Evolution (B23)].
1
Term
2
Credits
-
Kazumasa Tanaka
In this course, we will go over fundamental issues in neural mechanisms of learning and memory, with a focus on memory. In addition to lecture sessions, we will have a journal club and a study section. In the first week of the course, the instructor will list papers for the journal club. Each student chooses one of them and discusses it during the later session. In the study section, students will practice writing a research proposal (as a format of grant) and how to score proposals. The instruction will be provided in the first session.
3
Term
2
Credits
-
Tomomi Kiyomitsu
Genetics is the study of biologically inherited traits. Thus, genetics is related to all living organisms and more or less to all life science fields. In addition, modern genetic technologies not only have a strong impact on basic research, but on applied research as well, such as medicine and agriculture. This course introduces the key concepts of genetics and modern genetic technologies. In parallel to the lectures, students will see or experience CRISPR/Cas9-based genome editing using cultured human cells. Lecturers from outside of OIST may also be invited. The major goal of this course is to learn the key concepts of genetics and modern genetic technologies in order to utilize them and deal with their associated problems.
Prior knowledge
This course no prerequisite courses.
Suggested to take this course alongside/after B27 Molecular Biology of the Cell
1
Term
2
Credits
-
Vincent Laudet
Fish are an integral part of our societies and culture. In addition to their economic value, they have considerable cultural and symbolic value. However, because of their aquatic way of life, in an environment which is almost completely foreign to us, their zoological distance from us, from their cold blood and their bodies covered with scales, we often consider them as dumb creatures of little interest except their delicate flesh and sometimes their beautiful colors.
And yet! What diversity! What incredible abilities! What fantastic adaptations! Fish have conquered virtually all the aquatic environments of our planet, even the most extreme. They are capable of elaborate behaviors and of complex social interactions and they can express individual characters like shyness or boldness. Like birds, they communicate with elaborate sounds. We have too largely ignored these fascinating animals because we misunderstand them and their environment. This course will therefore consider in general terms the biology and evolution of fish from either marine or freshwater origin. It will allow the students to better understand how fish work, how they evolve, how they adapt to diverse environments and the challenges they face.
Prior knowledge
- The students must have taken the course "Coral Reef Biology and Ecology" from Tim Ravasi
- Preferably they will also have taken basic introductory course on ecology/evolution.
3
Term
2
Credits
-
Timothy Ravasi
Among the most spectacular of all ecosystems, coral reefs form in the world’s tropical oceans through the action of animals and plants. They are the largest and most complex biological structures on earth. Although they cover less than one percent of the earth’s surface, they are reservoirs for much of the ocean’s biodiversity, housing some of nature’s most intricate ecological secrets and treasures. Coral reefs are also the most productive ecosystems in the sea and provide significant ecological goods and services, estimated at about $375 billion annually with more recent estimates topping 9 trillion dollars in 2019.
Their physical structures protect thousands of miles of coastline from the fury of tropical storms, tsunamis, and many low lying islands threatened by rising seas. The intricate adaptations for survival that have evolved over an immense span of time make reefs vulnerable to human activities. For example, excess nutrients support algal overgrowth, while over fishing alters the food web. The extent to which reefs in remote locations are now showing signs of stress, especially bleaching and disease, points to the critical role that coral reefs play as indicators of declining ocean health. This course will be an introduction to tropical coral reefs and the organisms and processes responsible for their formation. We will begin with an overview of reefs and their tropical marine environment. The course will then move into the evolution, systematic, and physiology, ecology and symbiosis of reef building corals. These subjects will set the stage for learning about coral reef fish communities structure and ecological dynamics. The course will close by taking a critical look at natural and human disturbances to reefs with an emphasis on current models of management and conservation. This will allow us to examine cutting-edge questions in coral reef biology and conservation.
Target Students
Students with background in general biology or marine science who wish to become tropical marine biologists specializing in coral reefs and coral reef fish.
Prior knowledge
Students need to have basic knowledge of general biology and zoology. Also students have to able to swim and snorkel for the field trip component of the course.
3
Term
2
Credits
-
Emile Touber
Many physical processes exhibit some form of nonlinear wave phenomena. However diverse they are (e.g. from engineering to finance), however small they are (e.g. from atomic to cosmic scales), they all emerge from hyperbolic partial differential equations (PDEs). This course explores aspects of hyperbolic PDEs leading to the formation of shocks and solitary waves, with a strong emphasis on systems of balance laws (e.g. mass, momentum, energy) owing to their prevailing nature in Nature. In addition to presenting key theoretical concepts, the course is designed to offer computational strategies to explore the rich and fascinating world of nonlinear wave phenomena.
By the end of this course, participants dealing with wave-like phenomena in their research field of interest should be able to identify components that can trigger front-like structures (e.g. shocks, solitons) and be able to explore their motion numerically. Whilst the course is aimed at graduate students with an engineering/physics background, biologists interested in wave phenomena in biological systems (e.g. neurones, arteries, cells) are also welcome. However, it is assumed that participants have prior knowledge of maths for engineers and physicists.
Prior knowledge
Prior knowledge of maths for engineers and physicists.
2
Term
2
Credits
-
Liron Speyer
Students will learn the basic structure theory of simple Lie algebras over the complex numbers, as well as the theory of their highest weight representations. This will develop students' understanding of these fundamental objects in algebra, and give them some hands-on experience computing representations and proving some powerful (and quite beautiful!) results.
Students will learn the basic structures of simple Lie algebras over the complex numbers, including classification, root systems, Cartan subalgebras, Cartan/triangular decomposition, Dynkin diagrams, Weyl groups, and the Killing form. We will develop a highest weight theory of representations, including Verma modules and enveloping algebras. We will end with Weyl's character formula for finite-dimensional simple modules.
This is an alternating years course, taught first in AY2020, and subsequently in AY2022, etc.
Prior knowledge
A solid grasp of undergraduate linear algebra, as well as experience following long proofs and constructing your own proofs. Students must be very comfortable with proofs in order to understand the material in this course and complete the homework questions adequately. If you are unsure, please discuss this further with your academic mentor. Some prior knowledge of the representation theory of finite groups will also be helpful when grappling with analogous results for Lie algebras, but it is not completely necessary.
1
Term
2
Credits
-
Sam Reiter
Naturalistic animal behavior is complex. Traditionally, there have been two general approaches to dealing with this complexity. One approach, common in psychology, is to simplify an animal’s environment, or its movements, in order to make precise measurements. Another approach, taken by ethologists, is to study complex naturalistic behaviors directly. In many cases this choice has forced researchers to give up on quantitative rigor. Recent breakthroughs in camera technology and computational techniques open up the possibility of merging these approaches. We can now describe naturalistic behavior quantitatively.
Students will be expected to engage with the material, and discuss with their peers and the instructor during class. Homework is in the form of reading papers that will be discussed the following class (~2 hours/week), and in learning the background concepts necessary to understand and discuss the papers (~2 hours/week). Projects ideas will be proposed in writing ~2/3 way through the course (citing the relevant literature), and project results will be presented to the class. Projects will be assessed based on how they demonstrate the student’s mastery of the relevant course material, creativity, and on presentation quality.
Prior knowledge
The material builds on basic knowledge of linear algebra, machine learning, neuroscience, and behavioral ecology. A background in any of these topics isn’t required if a student is willing to learn the relevant concepts as they arise, in preparation for discussing papers in class. Suggested to take B26 first.
3
Term
2
Credits
-
Marco Terenzio
During this course we will review receptor signaling and its associated transcriptional responses as well as peripheral local translation of signaling molecules. We will discuss the active mechanisms of transport utilized by the neurons to convey organelles and signaling complexes from the plasma membrane to the nucleus with a focus on the dynein machinery and retrograde axonal transport. We will then review the current state of progress in the understanding of the link between defects in axonal trafficking and neurological diseases and between local translation of the response to axonal injury and the induction of a regenerative program. In this context we will discuss both the peripheral and central nervous system. This course will include a practical session of imagining of axonal transport, where the students will be exposed to the most recent techniques for imaging and quantifying intracellular transport.
This course is targeted to students who want to deepen their understanding of neuronal axonal signalling and get some hands-on experience in intracellular trafficking live imaging.
Prior knowledge
This course is an advanced course for neuroscience. It assumes a basic knowledge of cellular biology and neurobiology.
Passing the “Introduction to Neuroscience course” or equivalent previous course work elsewhere is required.
3
Term
2
Credits
-
Marco Edoardo Rosti
Students who complete this course will be able to:
understand the most common techniques for the numerical solution of partial differential problems (such as finite differences and finite volumes),
evaluate and comment on the stability and convergence of the numerical methods,
and solve numerically diffusion, convection and transport problems in multiple dimensions.
Target Students: Students interested in solving partial differential equations numerically and in understating possibilities and limitations of numerical techniques. Students should have a general knowledge of partial differential equations.
Prior knowledge
Students should have a general knowledge of partial differential equations. such as from the course B28.
A basic knowledge of Python, MATLAB or any other programming language is preferred but not essential.
3
Term
2
Credits
-
Yabing Qi
Surface science is a discipline devoted to elucidating fundamental properties of physics and chemistry occurring at surfaces and interfaces. Surface science contributes to many areas of science and technology, for example, physical chemistry, electronic devices, catalysis, semiconductor processing, new materials development, biomaterials, biotechnology and biomedicine, nanotechnology, and so on. This course is intended as an introduction to surface science basic concepts and instrumentation for graduate students. The objectives are twofold: (i) provide students with comprehensive lectures of basic concepts and operation principles of major analytical techniques in surface science and (ii) discussion of the applications of these concepts and instruments in various research fields.
Prior knowledge
General knowledge in physics and chemistry.
2
Term
2
Credits
-
Tomoki Fukai
Modern technologies in experimental sciences and computational sciences generate a large amount of high-dimensional data. In contrast to classical hypothesis testing, in modern statistical methods data are used to construct a statistical model that generated the dataset. Such a model not only describes the statistical properties of the past observations but also enables the prediction of future observations. The course is designed for students who wish to learn the mathematical methods to analyze high-dimensional data for active inference.
This course is based on the second half of the previous course "B07 Statistical Methods".
By completing this course, students will receive one credit.
This course can be taken immediately after the course B31 Statistical Testing, or as a stand-alone course.
Prior knowledge
Basic knowledge and skills in linear algebra (matrix, eigenvalues, eigenvectors, etc.), calculus (differentiation and integration of functions), and probability theories (Gaussian and some other distributions) are required. Skills in Python programming are also required for exercises.
2
Term
1
Credits
-
Tomoki Fukai
In this course, I will explain the basic methodology for hypothesis testing. Statistical analyses are required in many experimental and simulation studies. I will deliver lectures and exercises on the basics of probability theories and statistical methods including sample means, sample variances, p-values, t-test, u-test, Welch test, confidence intervals, covariance, ANOVA, multivariate analyses, correlations, information theory, mutual information, experimental design, and so on. After the course work, the students will acquire basic knowledge of hypothesis testing.
This course corresponds to the first half of the previous course "B07 Statistical Methods".
Prior knowledge
Students are expected to have basic knowledge of elementary mathematics such as differentiation, integration, and elementary linear algebra. However, whenever necessary, mathematical details will be explained.
Students will need to write some code in Python.
2
Term
1
Credits
-
Ryota Kabe
This course covers the essential knowledge and technology of organic optoelectronics. Since the organic optoelectronics is an interdisciplinary research area, we need to understand organic molecular design and synthesis, purification, basic photophysics, device design and fabrications, and device physics. This course introduces the basics of all of these topics.
Prior knowledge
undergraduate level chemistry knowledge
1
Term
2
Credits
-
Liron Speyer
We will cover the basics of linear algebra, including proofs. This will start from the perspective of vector spaces over arbitrary fields, quickly specialising to real and complex vector spaces. We will study linear maps between vector spaces, how these can be realised as matrices, and how this can be applied to solving systems of linear equations.
Alternating year course, first taught AY2021 (and again in AY2023, etc.)
Prior knowledge
Familiarity with real and complex numbers will be assumed. Ideally, students will have had some previous exposure to mathematical proofs, though this is not strictly required.
1
Term
2
Credits
-
Satoshi Mitarai
This course will develop student understanding of key components of the Earth system, as well as past and future variability. Topics to be covered include, but are not limited to, global energy balance, atmospheric circulation, surface winds and ocean circulation, deep-sea thermohaline circulation, Holocene climate, the El Niño Southern Oscillation, projections of future atmospheric CO2 and other greenhouse-gas concentrations, and the effects of climate change on marine environments. Hands-on exercises using predictions of the latest atmosphere-ocean coupled general circulation models will be employed to assess how climate change affects oceanic environments, e.g., based upon IPCC future climate change scenarios and past climate records. This course is open to any student, while it mainly targets marine science students. Basic mathematical knowledge (calculus) will be required. Students are expected to apply the skills they acquire in this class to their own marine biological and/or ecological studies to describe the influence of climate change on ocean environments quantitatively, and also to discuss potential outcomes for marine ecosystems on which their own research is focused.
For the project, students will analyze predictions of CMIP (Coupled Model Inter-comparison Project) models to assess the effects of climate change on marine environments, and write a brief report (a few pages) including some figures.
Prior knowledge
B28 Elementary Differential Equations and Boundary Problems and/or A104 Vector and Tensor Calculus
3
Term
2
Credits
-
Keiko Kono
We will read through the textbook “Molecular Biology of the Cell”, one chapter per class.
Students will work through the Problems workbook on their own as needed, but Professor Kono offers Office hours every Friday for student help.
Three small examinations will be required during the term, weighted 25%, 25%, and 50%.
The first two exams cover material up to that time. The final exam covers all material of the term.
Grade expectations are
A corresponds to scores of 85-100%
B corresponds to scores of 70-84%
C corresponds to scores of 60-69%
Scores less than 60% receive a fail grade.
Prior knowledge
The course is very basic. Non-biology students are welcome.
1
Term
2
Credits
-
Reiko Toriumi
The course is designed as an introduction to the methods of statistical mechanics and evolves into critical phenomena and the renormalization group.
The analogy between statistical field theory and quantum field theory may be addressed throughout the course.
Key concept which will be emphasized in this course is universality; we concern systems with a large number of degrees of freedom which may interact with each other in a complicated and possibly highly non-linear manner, according to laws which we may not understand. However, we may be able to make progress in understanding behavior of such problems by identifying a few relevant variables at particular scales. The renormalization group addresses such a mechanism.
Some selected topics are planned to be covered, such as conformal field theory, vector models/matrix models, SLE.
Prior knowledge
Students should have knowledge of Classical Mechanics and Quantum Mechanics to advanced undergraduate level.
1
Term
2
Credits
-
Yoshinori Okada
After overviewing various interesting quantum materials and their unique functionalities, this course will introduce the concept of materials design and its realization in bulk single crystal growth and epitaxial thin film growth. Then, the principles of single particle spectroscopy will be introduced, particularly focusing on photoemission and tunneling spectroscopy. This course is ideal for students interested in both crystal growth and spectroscopy in quantum materials science.
During this course, several lectures by external scientists and engineers from R&D companies will be arranged. Also, “4. Group discussion and presentations based on recent literatures” and “6. Experiencing quantum materials growth and their characterization” will be arranged acording to circumstances and students' preference.
Prior knowledge
Undergraduate level of condensed matter physics
3
Term
2
Credits
-
Yasha Neiman
We begin with a gentle and thorough introduction to Special Relativity, and take some time to have fun with shapes in Minkowski space. We proceed to an advanced treatment of relativistic particles, electromagnetic fields and weak gravitational fields (to the extent that doesn’t require General Relativity). Antiparticles are introduced early on, and we put an emphasis on actions and phase space structures. We introduce the geometric concept of spinors, and the notion of spin for particles and fields. We discuss the Dirac equation and the resulting picture of the electron. We introduce conformal infinity. Time allowing, we discuss a bit of conformal field theory and some physics in de Sitter space.
Prior knowledge
Maxwell’s equations in differential form. Solving Maxwell’s equations to obtain electromagnetic waves. Quantum mechanics.
1
Term
2
Credits
-
Paola Laurino
During this course the student will experience a research project. We are planning to run enzyme evolution experiments generating a random library of enzyme mutants and selecting for improved activity in vivo (bacterial strains). Student will create variants of Kan Kynase enzymes. The enzymes will be selected for higher antibiotic resistance to assess improved variants. After a few rounds of evolution, advantageous mutations will be enriched in the variants pool and identified by sequencing. The enriched mutations will be highlighted in the protein structure and then analysed.
Prior knowledge
Undergraduate level biochemistry or molecular biology
1
Term
2
Credits
-
Eliot Fried
A geometrically oriented introduction to the calculus of vector and tensor fields on three-dimensional Euclidean point space, with applications to the kinematics of point masses, rigid bodies, and deformable bodies. Aside from conventional approaches based on working with Cartesian and curvilinear components, coordinate-free treatments of differentiation and integration will be presented. Connections with the classical differential geometry of curves and surfaces in three-dimensional Euclidean point space will also be established and discussed.
Prior knowledge
multivariate calculus and linear (or, alternatively, matrix) algebra
1
Term
2
Credits
-
Jeff Wickens
The aim of this course is to engage students in thinking about and discussing fundamental issues in research on neural mechanisms of learning and memory. Topics include the neural mechanisms of learning, memory, emotion, and addictive behavior. Students will be expected to read original reports including classical papers as well as recent advances. The course includes an experimental requirement in which students must design and conduct an experiment related to learning and memory mechanisms of the brain.
Prior knowledge
Students should have previously taken an undergarduate neuroscience course and at least one other basic neuroscience course at OIST; or have completed the equivalent by documented self-directed study.
2
Term
2
Credits
-
Jun Tani
The primary objective of this course is to understand the principles of embodied cognition by taking a synthetic neurorobotics modeling approach. For this purpose, the course offers an introduction of related interdisciplinary knowledge in artificial intelligence and robotics, phenomenology, cognitive neuroscience, psychology, and deep and dynamic neural network models. Special focus is given to hands-on neurorobotics experiments and related term projects.
Prior knowledge
Basic mathematical knowledge for the calculus of vectors and matrices and the concept of differential equations are assumed. Programming experience in Python, C or C++ is also required.
1
Term
2
Credits
-
Marylka Yoe Uusisaari
The course will start from the mechanisms of animal movement, including the evolutionary, ecological and energetic aspects; we will explore the anatomical and mechanical features of the body machinery (such as muscles, bones and tendons) before investigating the structure and dynamic function of the neuronal circuits driving and controlling movements. We will thus examine neuronal function at various levels, allowing the students to familiarize themselves with many fundamental concepts of neuroscience; the theoretical lectures will be complemented by practical exercises where the students will study movement in themselves and their peers in the motion capture laboratory environment as well as with more classical approaches.
Prior knowledge
This is a basic level course, which will be adjusted according to the interests of enrolled students. No prior knowledge assumed, and suitable for out-of-field students.
However, the course B26 Introductory Neuroscience is required if you intend to continue with additional Neuroscience courses.
1
Term
2
Credits
-
Tom Bourguignon
Life sciences have been greatly influenced by the progress of DNA sequencing technologies. The field of Evolutionary Biology is no exception, and increasingly relies upon fast generation of DNA sequences, that are analysed using fast evolving bioinformatics tools. The aim of this course is to introduce the basic concepts of molecular evolution to students of all scientific backgrounds. We will explore some important questions in Biology, and through concrete examples, determine how molecular evolution theory help answering them. The students will also learn how to use a number of widely used bioinformatics tools.
Prior knowledge
Assumes general knowledge in biology
3
Term
2
Credits
-
Kenji Doya
The course starts with basic programming using Python, with some notes on other computing frameworks. Students then get acquainted with data manipulation and visualization using “numpy” and “matplotlib.” After learning how to define one’s own function, students learn iterative methods for solving algebraic equations and dynamic simulation of differential equations. The course also covers basic concepts in stochastic sampling, distributed computing, and software management. Toward the end of the course, each student will pick a problem of one’s interest and apply any of the methods covered in the course to get hands-on knowledge about how they work or do not work.
Prior knowledge
Prerequisite courses and assumed knowledge: Basic computer skill with Windows, MacOS, or Linux is assumed. Each student will bring in a laptop provided by the Graduate School. Knowledge of basic mathematics, such as the calculus of vectors and matrices and the concept of differential equations, is assumed, but pointers for self-study are given if necessary.
1
Term
2
Credits
-
Izumi Fukunaga
The course will cover general concepts and specific modalities as detailed in the table below. Classes alternate between lecture-style teaching and a journal club. Each lecture will be based on a textbook chapter (Kandel et al’s Principles of Neural Sciences, in combination with other specialised books described in the “Textbooks” section) to cover basic and broad topics, but will also serve as an opportunity to introduce concepts required to understand the research article associated with the lecture.
Prior knowledge
The course is aimed at students with a background in neuroscience (either at the BSc/MSc level or having successfully completed some of the basic neuroscience course offered at OIST). It assumes knowledge in cellular neurophysiology and neuroanatomy. Most relevant courses at OIST will include: B26 (Introduction to Neuroscience, required); B05 (Cellular Neurobiology; desirable), A405 (Emerging technologies in life sciences; desirable), B09 (Learning and behaviour; desirable), A310 (Computational neuroscience; highly desirable).
B26 is the most important (in terms of subject matters listed on the course’s website), so a pass in this course (or in the exam, without taking the course) is a pre-requisite.
3
Term
2
Credits
-
Yasha Neiman
We begin by introducing tensors in non-relativistic physics. We then give an overview of Special Relativity, and discuss the special nature of gravity as an “inertial force”. With this motivation, we develop the differential geometry necessary to describe curved spacetime and the geodesic motion of free-falling particles. We then proceed to Einstein’s field equations, which we analyze in the Newtonian limit and in the linearized limit (gravitational waves). Finally, we study two iconic solutions to the field equations: the Schwarzschild black hole and Friedman-Robertson-Walker cosmology. We will use Sean Carroll’s textbook as the main reference, but we will not follow it strictly.
This is an alternating years course.
Prior knowledge
Prerequisites: Maxwell’s equations in differential form. Solving Maxwell’s equations to obtain electromagnetic waves. Linear algebra of vectors and matrices.
3
Term
2
Credits
-
Simone Pigolotti
This course presents a broad introduction to stochastic processes. The main focus is on their application to a variety of modeling situations and on numerical simulations, rather than on the mathematical formalism. After a brief resume of the main concepts in probability theory, we introduce stochastic processes and the concept of stochastic trajectory. We then broadly classify stochastic processes (discrete/continuous time and space, Markov property, forward and backward dynamics). The rest of the course is devoted to the most common stochastic processes: Markov chains, Master Equations, Langevin/Fokker-Planck equations. For each process, we present applications in physics, biology, and neuroscience, and discuss algorithms to
simulate them on a computer. The course include “hands-on” sessions in which the students will write their own Python code (based on a template) to simulate stochastic processes, aided by the instructor. These numerical simulations are finalized as homework and constitute the main evaluation of the course.
Prior knowledge
• Basic calculus: students should be able to calculate integrals, know what a Fourier transform is, and solve simple differential equations.
• Basic probability theory: students should be familiar with basic probability theory, e.g. discrete and continuous distributions, random variables, conditional probabilities, mean and variance, correlations. These concepts are briefly revised at the beginning of the course.
• Scientific programming: the students are expected to be already able to write, for example, a program to integrate a differential equation numerically via the Euler scheme and plot the results. Python is the standard language for the course. The students are required to install the Jupiter notebook system and bring their own laptop for the hands-on sessions.
3
Term
2
Credits
-
Yejun Feng
Condensed matter physics originates from solid state physics in 1950’s and evolves into a subject which focuses on collective behavior, symmetry, and topological states. Over the past century, this sub-field of physics has grown with many various ramifications that any offered perspective would always be partial and biased. Nevertheless, I would like to limit this class to the introduction level, and give a broad description of the field. For a few topics, I will try to demonstrate how to evolve from fundamental concepts to perspectives of advanced topics.
Prior knowledge
Students are suggested to have basic (undergraduate) understanding of quantum mechanics and statistics.
2
Term
2
Credits
-
Thomas Busch
The course will start out by introducing the fundamental ideas for cooling and trapping ultracold atoms and review the quantum mechanical framework that underlies the description of interacting matter waves in the ultracold regime. This will introduce the idea of degenerate Bose and Fermi gases, and in particular the concept of Bose-Einstein condensation.
After this the main properties of Bose-Einstein condensates will be discussed, including coherence and superfluidity, and for Fermi gases the physics of the BCS transition will be introduced. Conceptually important developments such as optical lattices, Feshbach resonances, artificial gauge fields and others will be explained in detail as well. New developments in the area of strongly correlated gases will be introduced and applications of cold atoms in quantum information or quantum metrology provide the final part of the course.
The course will mostly focus on the theoretical description of ultracold quantum gases, but regularly discuss experimental developments, which go with these.
Prior knowledge
While the fundamental concepts of atomic physics and quantum mechanics that are required will be reviewed in the beginning of the course, basic prior knowledge of quantum mechanics is required (e.g. undergraduate quantum mechanics).
Companion course to A211 Advances in Atomic Physics
2
Term
2
Credits
-
Denis Konstantinov
This is a two-term graduate course that covers most of the essential topics of modern nonrelativistic quantum mechanics. The course is primarily intended for graduate students with background in Physics.
Prior knowledge
Students who take the course are expected to be familiar with general topics in Classical Mechanics, Electrodynamics and Calculus. This course requires a pass in A216 Quantum Mechanics I.
2
Term
2
Credits
-
Denis Konstantinov
This is a two-semester graduate course that covers most of the essential topics of modern non-relativistic quantum mechanics. The course is primarily intended for graduate students with background in Physics and aims to prepare such students for taking further advanced courses in Physics and Material Science offered in OIST, such as Solid State and Condensed Matter Physics, Advanced Quantum Mechanics, Advances in Atomic Physics, Quantum Field Theory, etc.
Students who take this course are expected to be familiar with general topics in Classical Mechanics, Electrodynamics and Calculus.
Prior knowledge
Students who take the course are expected to be familiar with general topics in Classical Mechanics, Electrodynamics and Calculus.
1
Term
2
Credits
-
Akihiro Kusumi
Description: Students will learn several basic concepts of biophysics including thermal conformational fluctuation and thermal diffusion, and how cells might take advantage of these physical processes to enable their functions. As a biological paradigm, the cellular membrane system (and their functions), with a special attention paid to signal transduction in the plasma membrane, will be extensively covered. This is because the membranes are critically important for a variety of cellular processes, in the fields of cancer biology, immunology, neuroscience etc., and also because the membrane system provides us with an interesting and useful biological paradigm to learn how the life processes are made possible by thermal-physical processes. As a way of directly “seeing” the thermal, stochastic processes exhibited by receptors and downstream signaling molecules undergoing signaling in live cells, the methods of single-molecule imaging-tracking and manipulation will be discussed quite extensively. Through this course, students will better understand the interdisciplinary field of biology, chemistry, physics, and mathematical science.
Prior knowledge
Biology, chemistry, or physics at undergraduate levels
1
Term
2
Credits
-
Mitsuhiro Yanagida
Cells undergo aging and have limited lifespans. This lecture course covers the genetic, molecular, and cellular mechanisms that control cellular aging and that affect the lengths of organismal lifespans. Various strategies for investigating human longevity are also discussed.
Prior knowledge
Requires at least advanced undergraduate level Cell Biology and Genetics or similar background knowledge
3
Term
2
Credits
-
Yohei Yokobayashi
In this course, students will learn basic principles of nucleic acid chemistry and engineering through lectures and discussions. The students will then use the basic knowledge to deepen their understanding of the current research in the field of nucleic acid chemistry and engineering. Emphasis will be placed on reviewing current and future applications of nucleic acids in diverse fields including chemistry, biology, materials, medicine, biosensors, and engineering. Finally, the students will design, construct, and characterize functional nucleic acids in the laboratory while learning basic experimental skills to manipulate nucleic acids.
2
Term
2
Credits
-
Julia Khusnutdinova
In this course, students will learn basic principles of electrochemistry with a particular focus on redox behavior of transition metals including metalloproteins. Modern research in application of transition metal complexes for renewable energy storage and production will be highlighted and discussed in detail, including metal-catalyzed water oxidation, proton reduction and CO2 reduction processes. The course will provide practical training in voltammetric techniques and spectroelectrochemistry, and analysis and simulation of cyclic voltammetry data.
See course highlights at: https://groups.oist.jp/cccu/post/2016/12/16/course-highlights-inorganic-electrochemistry.
1
Term
2
Credits
-
Hiroshi Watanabe
This course will provide an introduction to Evolutionary Biology focusing on the developmental process of multicellular organisms for students with and without an undergraduate background in this field. Two major goals in this course will be to understand evolutionary changes in development and to learn modern creatures and technologies employed for addressing issues in evolutionary developmental biology. This course presents the basic principles and recent findings in evolutionary developmental biology.
Prior knowledge
No prior knowledge assumed
3
Term
2
Credits
-
Amy Shen
The interface between engineering and miniaturization is among the most intriguing and active areas of inquiry in modern technology. The aim of this course is to illuminate and explore microfluidics as an interdisciplinary research area, with an emphasis on emerging microfluidics disciplines, including molecular assembly to bulk and device level scales, with applications in novel materials synthesis, bio-microtechnology and nanotechnology.
The course will begin by highlighting important fundamental aspects of fluid mechanics, scaling laws and flow transport at small length scales. We will examine the capillary-driven, pressure-driven, and electro-kinetic based microfluidics. We will also cover multi-phase flow, droplet-based microfluidics in microfluidics. This course will also illustrate standard microfabrication techniques, micro-mixing and pumping systems.
Prior knowledge
A good pass in B13 Fluid Mechanics is required pre-knowledge for A212. If you have taken Fluid Mechanics from your former B.S or M.S Universities, please contact Prof. Amy Shen directly to determine whether you are prepared to take A212.
3
Term
2
Credits
-
Students will be placed in a laboratory appropriate to their specific needs in terms of language and location. They will participate in laboratory life on a full-time basis to learn the skills and techniques of that laboratory, to become familiar with laboratory behavior, and to receive training in scientific practice and routine.
All
Term
1
Credits
-
Research students from countries other than Japan may have limited ability in the Japanese language. While the teaching and research language used at OIST is English, the availability of English outside the OIST campus is limited. Essential Japanese for Foreign Researchers is an optional course for students from Non-Japanese-speaking countries.
This course aims to ensure competence in Japanese language sufficient for working in a laboratory in Japan. It includes basic Japanese language equivalent to at least the Japanese Language Proficiency Test level N5. In addition, it will include a course on reading Japanese for laboratory safety, including important signs and labels found on scientific instruments in laboratories. Students will learn beginner to intermediate level Japanese in an immersive learning environment, focusing on practical Japanese for foreigners in Japan.
All
Term
1
Credits
-
Academic technical English is a specialized area with particular requirements for clarity in the communication of difficult concepts. Research students for whom English is not their primary language may need to develop their English language proficiency to enable more effective communication in the areas of science and technology.
English for Higher Education in Science and Technology is an optional course for those students for whom English is a second language, and will provide training and practice in technical and scientific English, allowing students to participate more effectively in the OIST program, and beyond. English for Higher Education in Science and Technology
covers such topics as academic vocabulary, critical reading and understanding of academic text, listening skills, paraphrasing and summarizing scientific text, delivery of presentations, participation in academic debates and discussions, research skills including electronic research and presentation of results, and structuring an argument for academic texts and essays.
All
Term
1
Credits
-
Students work in the laboratory of the Professor under whom they wish to conduct their thesis research. They undertake and write up preliminary research work, complete an in-depth literature review and prepare a research plan. The preliminary research work should include methods the students will use in their thesis research. The literature review should be in the area of their thesis topic and be of publishable quality. The research plan should comprise a projected plan of experiments to answer a specific question(s) and place the expected outcomes against the current state of knowledge, and should take into account the resources and techniques available at OIST. The research data generated in this proposal may be included in the subsequent doctoral thesis, if appropriate.
All
Term
0
Credits
-
Course Requirements
Prepare a written summary of the aims of the rotation. Students will study original publications and discuss with the Professor in charge of the research unit to prepare the aims. The summary should be no more than one page including references and illustrations. The proposal should be submitted to the graduate school (web form). Due date is the last day of the first calendar month of term.
Undertake the activities in the research unit to fulfill the aims of the rotation. The activities should be completed during the term of the rotation.
Participate in research unit meetings and seminars during the rotation. The student is expected to attend and as appropriate, ask questions, and join discussions.
Present the results of the rotation activity as an oral presentation to the laboratory members. One of the three rotations will be presented as an oral presentation to the entire class as a part of Professional Development.
Submit a written report on the rotation (web form). It is understood that results cannot be expected in so short a time, but the background, including a short literature review, methods used, and activity carried out in the research unit should be described using the scientific language of the field. The report is due on the last day of the final month of term.
Each student will do a minimum of three laboratory rotations, one per term.
Selection of Rotations
All students will undertake at least three rotations. Assignment of rotations is made by the Graduate School, following information provided by the student in the Academic Plan. Final approval of the selection of rotations will be given by the Dean, taking into account the availability of supervision and the overall program of the student. At least one of the rotations shall be outside the specific field of the student’s academic background.
All
Term
0
Credits
-
Hiroki Ishikawa
In this course, students will learn basic principles of immunology including the cellular and molecular mechanism of innate and adaptive immunity. The course also provides the clinical importance of immunology in various diseases such as HIV/AIDS, autoimmunity and allergy. Then, students will learn how the immune response can be manipulated by vaccination to combat infectious diseases and cancer.
3
Term
2
Credits
-
Gustavo Gioia
Students are introduced to the concepts of stress and strain, and discuss conservation laws and constitutive equations. We derive the Navier equations of linear elasticity, introduce the Airy stress-function method, and solve problems to illustrate the behavior of cracks, dislocations, and force-induced singularities in applications relating to materials science, structural engineering, geophysics and other disciplines.
Prior knowledge
Prerequisite is A104 Vector and Tensor Calculus
3
Term
2
Credits
-
Pinaki Chakraborty
We will introduce basic concepts of flow of fluids. We will discuss conservation laws and constitutive equations. We will derive the Navier-Stokes equations, and study its exact and approximate solutions. Last, we will introduce the theory of hydrodynamic stability and then discuss turbulent flows. Throughout the course we will discuss a wide spectrum of flows from nature and engineering.
Prior knowledge
Prerequisite is A104 Vector and Tensor Calculus
3
Term
2
Credits
-
Nic Shannon
Matter can exist in many different phases. The aim of this course is to explain why, and how one phase can transform into another. Starting from the question “what is temperature?”, the ideas of entropy, free energy, and thermal equilibrium are introduced, first in the context of thermodynamics, and then as natural consequences of a statistical description of matter. From this starting point, a simple physical picture of phase transitions is developed, with emphasis on the unifying concept of broken symmetry. The course is designed to be accessible to students from a wide range of educational backgrounds. It will be assessed through weekly problem sets, and a final presentation on a modern example of the application of statistical physics ideas, chosen by the student.
2
Term
2
Credits
-
Tsumoru Shintake
A graduate course in analytical mechanics, covering the essential equations and their applications, to prepare for later courses in electrodynamics and quantum physics. This course assumes undergraduate level knowledge of mechanics and a firm grasp of calculus and vector mathematics. An understanding of static electromagnetic fields is extended through Maxwell’s equations to a discussion of dynamic vector fields and electromagnetic waves. Along the way, numerous physical and technical applications of these equations are used to illustrate the concepts, including dielectrics and conductors, wave guides, and microwave engineering. Special relativity is introduced with discussion of relativistic and non-relativistic motion and radiation, using linear accelerators and synchrotron radiation as illustrative applications.
2
Term
2
Credits
-
Mahesh Bandi
Mastery of the concepts and techniques of analytical mechanics is essential to a deep understanding of physics. This course begins with basic principles and proceeds to the Newtonian equations of motion and laws of conservation. We use the Lagrange formalism to describe particle motion in multiple modes, before covering the equations of Euler and Hamilton, and canonical transformations. The calculus of variation is used to develop Maupertuis’s principle and the Hamilton-Jacobi equations, providing a starting point for the consideration of waves in later courses. This course is taught from the unifying principles of symmetry and least action.
1
Term
2
Credits
-
Gail Tripp
*not offered for 2018 and 2019
This course aims to introduce the function of the brain at the macroscopic level, namely, the control of behaviors and the cognitive and adaptive mechanisms behind it. The topics include the following: Reflex, classical and operant conditioning. Perception, adaptation, and attention. Feedback and predictive control. Procedural and declarative memory. Motivation and emotion. Thinking and reasoning. Communication and language. Psychological disorders. Clinical and experimental neuropsychology.
2
Term
2
Credits
-
Bernd Kuhn
Principles of physics of central relevance to modern biological analysis and instrumentation are introduced with an emphasis on application in practical research areas such as electrophysiology, optogenetics, electromagnetics, the interaction of light and matter, and brain recording, stimulation, and imaging.
2
Term
2
Credits
-
Tomoyuki Takahashi
In this course students learn about the cellular and molecular basis of neuronal functions, and how individual electrical signals are integrated into physiological functions. The course is a combination of student-led presentations on each of the key topics, and also student presentations of several classic papers, and a series of laboratory explorations of the topics covered in class.
3
Term
2
Credits
-
Matthias Wolf
The course is designed as a mix of introductions into selected topics in the theory of transmission electron microscopy followed by practical demonstrations and hands-on exercises, which provide an opportunity to comprehend the concepts by experimenting with commonly-used image processing software. Students will be required to read and digest scientific papers for a subset of lecture topics on their own, which will subsequently be discussed jointly during student presentations with the goal to immerse them into the subject without passive consumption. The lectures cover several important concepts of the physics of image formation and analysis, which require a basic level of mathematics. An emphasis will be given to highlighting common properties between diffraction and image data and how to take advantage of tools from both techniques during the final image processing projects.
Prior knowledge
Ideally combined with A410 Molecular Electron Tomography (Skoglund)
1
Term
2
Credits
-
Workshops, defined as residential short courses in particular topics in a specific scientific or mathematical discipline, and sometimes referred to as Summer Schools, or Winter Schools, etc., are a recognized means of undergoing intensive training in a specific topic or technique. In such workshops, some of the leading scientists in an area gather to share ideas, to keep each other up-to-date in the latest techniques and developments, and to teach senior students. Workshops for award of credit should comprise an intense period of lectures and exercise sessions during 10 working days or at least 40 hours of instruction, and be at a level that is accessible to doctoral students.
International workshops (which may be held in OIST, in Japan, or overseas) must be approved by the CEC as meeting criteria including sufficient content, quality of instruction and instructors, duration, and other criteria as may be deemed necessary. Preference for approval is given to workshops that include assessment and provide a transcript or report from the organisers to OIST.
Students who wish to receive credit for attending such a workshop should first seek approval (before booking travel and registration) from the Graduate School, who will check that the workshop meets approval criteria. The workshop must be appropriate and relevant to the student’s intended thesis research, and be endorsed by the thesis supervisor and academic mentor.
Please use the form at THIS LINK to apply.
All
Term
1
Credits
-
The course Special Topics will provide an opportunity for students to study topics concerning recent scientific breakthroughs, cutting edge research of topical interest, novel, state of the art technologies, and techniques not otherwise available, with leading international experts in those topics or technologies.
This course option must be conducted in collaboration with a faculty member to provide internal academic oversight and guidance, and will follow common guidelines to ensure the required academic standards are maintained.
Each Special Topics course will require the approval of the Dean before being offered.
Students will be required to obtain the approval of the Academic Mentor or Thesis Supervisor before taking the course, and complete a defined piece of work as part of the course.
All
Term
1
Credits
-
The course Independent Study will foster the development of independent study and research skills such as reading and critiquing the scientific literature, formulating scientific questions, and integrating knowledge into a coherent synthesis. Students will undertake a self-directed program of reading and synthesis of ideas. This course option must be conducted under the guidance of a faculty member acquainted with such work, and will follow common guidelines to ensure academic standards are maintained. Students should, in consultation with the faculty member, prepare a plan of the study, carry out the appropriate reading, and then describe the results of their study in a substantial report or essay. This course may be taken in any one term, and should be completed within the period of that term. The due date for all work, including online course completion, will be at the end of the current term.
The source material for Independent Study is now expanded to include online courses from a variety of educational sources, including Udacity, edX, and Coursera, subject to those courses being approved as relevant to the student's study and of sufficient educational merit. Seek approval before enrolling if credit is required. Total of external course credits permitted (online courses and international workshops) must be less than 50% of degree requirements. Your online course proposal must be approved by GS before you enroll in the online course, and the course fee can only be reimbursed AFTER completion with evidence (ex. completion certificate). Please contact Academic Program Team (academic-programs@oist.jp) for assistance with online course purchase, after your request is approved, but before making the payment.
Student and tutor should agree on the extent of supervision provided, such as timing and format of face-to-face meetings, progress checks, and so on, especially for online courses. This should be detailed in the proposal, and the student should commit to this undertaking.
A list of online courses previously approved for this purpose can be seen HERE, so check here first for examples of approved courses.
All
Term
1
Credits
-
Ichiro Maruyama
This course is designed to provide a broad, advanced-level coverage of modern technologies in life sciences for first year PhD students. Topics include recombinant DNA technologies, polymerase chain reactions, DNA sequencing, microfluidics, fluorescent proteins, optical microscopy, mass spectrometry among others. Lectures will draw from historical and current research literature with emphasis on development of technologies as life sciences make progresses. A major goal of this course is to help graduate students accustomed to inventing novel technologies, improving existing technologies, or formulating a novel idea in the field of life sciences.
3
Term
2
Credits
-
Erik De Schutter
Computational neuroscience has a rich history going back to the original Hodgkin-Huxley model of the action potential and the work of Wilfrid Rall on cable theory and passive dendrites. More recently networks consisting of simple integrate-and-fire neurons have become popular. Nowadays standard simulator software exists to apply these modeling methods, which can then be used to interpret and predict experimental findings.
This course introduces some standard modeling methods with an emphasis on simulation of single neurons and synapses and an introduction to integrate-and-fire networks. Each theoretical topic is linked to one or more seminal papers that will be discussed in class. A number of simple exercises using the NEURON simulator will demonstrate single neuron and synapse modeling.
Prior knowledge
Requires prior passes in OIST courses B22 Computational Methods and B26 Introduction to Neuroscience, or similar background knowledge in computational methods, programming, mathematics, and neuroscience.
2
Term
2
Credits
-
Hidetoshi Saze
Epigenetic regulation of gene activity is essential for development and response to environmental changes in living organisms. This course introduces fundamental principles and key concepts of epigenetics, and original research publications contributed to understanding the mechanism underlying the epigenetic phenomena will be reviewed. Lecturers from outside OIST may be invited for specific topics.
Prior knowledge
Requires at least advanced undergraduate level Cell Biology and Genetics or similar background knowledge
3
Term
2
Credits
-
Tadashi Yamamoto
This course consists of lectures and exercises. First, students learn, through lectures, recent progress in cancer research and the mechanism of carcinogenesis based on the molecular and cellular functions of oncogenes and anti-oncogenes. Further, students will learn the relevance of signal transduction, cell cycle progression, cell adhesion, and gene regulation to tumor development and are encouraged to simulate effective methods of diagnosis and treatment of cancer. Further, through exercises, students will consider the relevance of genome sciences and systems biology to cancer research. Students are encouraged to refer to the textbook and to papers from the current literature. The course will also present special novel and important topics from year to year.
Prior knowledge
Requires at least advanced undergraduate level Cell Biology and Genetics or similar background knowledge
1
Term
2
Credits
-
Yoko Yazaki-Sugiyama
The course provides an understanding of the neuronal mechanisms that underlie animal behavior. We will study the neuronal mechanisms for specialized animal behaviors such as sensory processing, motor pattern generation, and learning by reading original papers, which also provide an understanding of experimental technique. The course further discusses the evolutionary strategy and the biological ideas of animal behavior and underlying neuronal mechanisms.
Prior knowledge
Required: B26 Introduction to Neuroscience or similar (demonstrated by passing the B26 exam)
2
Term
2
Credits
-
Noriyuki Satoh
The course presents the most recent theory and techniques in evolutionary and developmental biology with an emphasis on the underlying molecular genomics. Recent advances in decoding the genomes of various animals, plants and microbes will be followed, with a discussion on comparative genomics, the evolution of transcription factors and signal transaction molecules and their relation to the evolution of the various complex body plans present through history.
3
Term
2
Credits
-
Ichiro Masai
This course introduces fundamental principles and key concepts in the developmental processes of animal organisms, by focusing on Drosophila embryonic development and vertebrate neural development as models, and will facilitate graduate students to reach a professional level of understanding of developmental biology. Furthermore, genetic tools for live imaging of fluorescence-labeled cells using Drosophila and zebrafish embryos will be introduced as practical exercises. The course also includes debate on specific topics in developmental biology by students and a writing exercise of mock-grant application. Some lecturers outside OIST will be invited to present particular special topics.
2
Term
2
Credits
-
Síle Nic Chormaic
Advanced level course in atomic physics. Progress in laser control of atoms has led to the creation of Bose-Einstein condensates, ultrafast time and frequency standards and the ability to develop quantum technologies. In this course we will cover the essentials of atomic physics including resonance phenomena, atoms in electric and magnetic fields, and light-matter interactions. This leads to topics relevant in current research such as laser cooling and trapping.
2
Term
2
Credits
-
Thomas Busch
Advanced course in Quantum Mechanics, based on recent theoretical and experimental advances. Evolution in Hilbert space and quantum bits; conditional quantum dynamics; quantum simulations; quantum Fourier transform and quantum search algorithms; ion-trap and NMR experiments; quantum noise and master equations; Hilbert space distances; Von Neumann entropy; Holevo bound; entanglement as a physical resource; quantum cryptography; lab: quantum eraser, interaction free measurement.
Prior knowledge
Solid undergraduate Quantum Mechanics preparation.
Students may wish to take this with A273 Ultracold Quantum Gasses and A218 Condensed Matter Physics.
2
Term
2
Credits
-
Keshav Dani
This course will be an introductory graduate level course to initiate students into the techniques of ultrafast spectroscopy. They will be introduced to the basic concepts underlying sub-picosecond phenomena in nature (ultrafast chemical processes, femtosecond electron dynamics in materials, etc.) and the tools used to study such phenomena (pump-probe spectroscopy, Terahertz Time Domain Spectroscopy, etc.).
1
Term
2
Credits
-
Fujie Tanaka
This course covers essential concepts and recent advances in the design and synthesis of functional molecules used for understanding and controlling biological systems. Topics of this course include design and synthesis of small organic molecules, organic reactions, methods for controlling reaction pathways, asymmetric synthesis, mechanisms of catalysis and molecular recognition, and creation of designer proteins and peptides.
2
Term
2
Credits
-
Shinobu Hikami
This course covers quantum field theory. Due to recent developments, we organize it with emphasizing statistical field theory.
The renormalization group method, symmetry breaking, gauge field and string theory, random matrix theory are key ingredients.
Prior knowledge
Solid undergraduate quantum mechanics preparation.
B11 Classical Electrodynamics
3
Term
2
Credits
-
Síle Nic Chormaic
Review of geometrical optics; wave properties of light and the wave equation; Helmholtz equation; wave optics, including Fresnel and Fraunhofer diffraction, transfer functions, coherence, auto and cross-correlation; Gaussian and non-Gaussian beam profiles; quantum optics and photon statistics; spin squeezing; applications of optics including fiber optics, laser resonators, laser amplifiers, non-linear optics, and optical trapping; quantum properties of light; interaction of photons and atoms.
2
Term
2
Credits
-
Jonathan Miller
This course develops advanced mathematical techniques for application in the natural sciences. Particular emphasis will be placed on analytical and numerical, exact and approximate methods, for calculation of physical quantities. Examples and applications will be drawn from a variety of fields. The course will stress calculational approaches rather than rigorous proofs. There will be a heavy emphasis on analytic calculation skills, which will be developed via problem sets.
Prior knowledge
Calculus, e.g. A104 Vector and Tensor Calculus or B28 Ordinary and Partial Differential Equations
2
Term
2
Credits
-
Kenji Doya
This course is based on a book KD is writing, "Brain Computation: A Hands-on Guidebook" using Jupyter notebook with Python codes.
The course will be in a "flipped learning" style; each week, students read a draft chapter and experiment with sample codes before the class.
In the first class of the week, they present what they have learned and raise questions.
In the second class of the week, they 1) present a paper in the reference list, 2) solve exercise problem(s), 3) make a new exercise problem and solve it, or 4) propose revisions in the chapter.
Toward the end of the course, students work on individual or group projects by picking any of the methods introduced in the course and apply that to a problem of their interest.
Students are assumed to be familiar with Python, as covered in the Computational Methods course in Term 1, and basic statistics, as coverd in the Statistical Tests and Statistical Modeling courses in Term 2.
Prior knowledge
Assumes good knowledge of statistics and ability to look at biological problems in a mathematical way.
OIST courses to complete beforehand: B31Statistical Tests and/or B32 Statistical Modeling
1
Term
2
Credits
