Starting between 

October 2024 to March 2025

Starting between 

April 2025 to September 2025

Faculty Research Interests and Project Information

Click here for more information on Research Activities at OIST.

We welcome interns enthusiastic about getting to know research in complexity science and evolution, including links with theoretical physics, applied mathematics, computer science, ecology, socio-economics, and game theory. Projects can be co-designed according to mutual interests in the following research areas and beyond:

1. Social dilemmas and governance of common goods
2. Biodiversity dynamics and speciation
3. Sustainable fisheries management and fishing-induced evolution
4. Systemic risk and network dynamics
5. Evolutionary community ecology and eco-evolutionary vegetation dynamics
6. Adaptive dynamics theory and models
7. Simplifying spatial complexity
8. Disease ecology and evolution

1.  swarm robotics to study the self-organization of social complexity;
2.  human-computer interaction studies to investigate how digital technology becomes incorporated   into our behavior;
3.  EEG hyperscanning experiments to study the neural underpinnings of social interaction;
4.  sensory substitution studies to investigate the dynamics of perceptual learning.

1. computational neuroscience models of cognitive function.
2. mathematical/biological mechanisms of learning.
3. analysis of neural activity data for exploring neural code.

Projects are available to study;

1. The organization and signaling of astrocytes that shape synaptic circuit function.
2. Mechanisms that implement synapse homeostasis.

We are interested in students who would like to do AI, GLLM, bioinformatics metagenomic, metabolic data analysis.

Quantum reference frames, observables in gauge theory and gravity, subsystems in gauge theory and gravity, spacetime thermodynamics, algebras and subregions, entropies in gravity, relational physics, quantum error correction, reconstructions of quantum theory.

1. Quantum state detection in electron-on-helium system for qubit implementation
2. Quantum CCD based on microchannel devices with electrons on superfluid helium

Our unit have investigated molecular and cellular mechanisms that regulate cell differentiation and organogenesis during development, using zebrafish as an animal model. Four research projects below are ongoing.

1. Mechanisms that regulate retinal neural circuit formation.
2. Mechanisms that regulate photoreceptor degeneration and regeneration.
3. Mechanisms that regulate lens fiber differentiation.
4. Mechanisms that regulate social behavior in zebrafish.

The Cell Proliferation and Gene Editing Unit aims to understand molecular mechanisms of cell division and mitotic quality control in normal and cancer cells to determine tumor-suppressive mechanisms and cancer-specific vulnerabilities that can be exploited in chemotherapeutic approaches (https://groups.oist.jp/cellpro).
Current Projects:

1. Investigating the consequences of mitotic memory on cancer cell proliferation.
2. Pan-cancer studies to identify cancer-specific vulnerabilities using genetic screens.

Our unit focus covers 3 broad overlapping theoretical themes:

• The theoretical design of quantum network technologies for a future quantum internet
• The development of hybrid quantum systems and their applications
• Quantum engineering

For interns we are willing to considered projects associated with the first two themes. In particular

• Exploration of quantum networking algorithms
• Exploration of hybrid quantum systems associated ensembles of atoms coupled to resonators and the potential properties than can be designed
• Applications of NISQ processors

Projects are available in the following fields:

1. Quantum computation (applications and hardware design)
2. Quantum devices
3. Quantum communications
4. Quantum complex systems

The Model-Based Evolutionary Genomics Unit works at the interface of computational and evolutionary biology. Currently, our two main focus areas are:
i) reconstructing the Tree of Life, including the history of early life forms by continuing to develop and apply state-of-art probabilistic models of molecular evolution and using machine learning to model co-evolutionary dependencies across biological scales to reconstruct ancient phenotypes and environments;
ii) understanding somatic evolution in hierarchically organised tissues and across the Tree of Life, both from a theoretical standpoint (e.g., To what extent has tissue organisation evolved to minimise somatic evolution humans? Why do both plants and animals have stem cells?) and from a more data orientated perspective (e.g., What can emerging data on genetic variation in healthy tissues tell us about tissue organisation, the emergence of tumours and ageing?).
Recent papers of ours concerning these topics include:

“A rooted phylogeny resolves early bacterial evolution” Coleman, Davin, Mahendrarajah, Szánthó, Spang, Hugenholtz*, Szöllősi*, Williams*
Science 2021
https://science.sciencemag.org/content/372/6542/eabe0511.abstract

"Divergent genomic trajectories predate the origin of animals and fungi"
Ocaña-Pallarès, Williams, …, Bapteste, Tikhonenkov, Keeling, Szöllősi, Ruiz-Trillo
Nature 2022
https://www.nature.com/articles/s41586-022-05110-4

“Trade-off between reducing mutational accumulation and increasing commitment to differentiation determines tissue organization” Demeter, Derényi, Szöllősi
Nature Communications 2022
https://www.nature.com/articles/s41467-022-29004-1

Experimental work concerning ion trapping and/or laser physics, or spins in diamonds. Potential research projects are,

1. Design, fabrication, and testing of a novel ion trap
2. Integration of a micro optical resonator in an ion trap
3. Ultra-narrow linewidth laser and its application to spectroscopy
4. Coupling a spin in diamond to an optical cavity

1. Focused ultrasound effects on the brain.
2. Mouse behavior in virtual reality.

Projects in machine learning and data science. Interns will work on developing machine learning algorithms and applying them to real-world problems. After the internship period, we aim to submit the intern work to top machine learning venues. The following papers are written by recent interns:

1. Haoyu Han, Xiaorui Liu, Li Ma, MohamadAli Torkamani, Hui Liu, Jiliang Tang, & Makoto Yamada. Structural Fairness-aware Active Learning for Graph Neural Networks. In ICLR, 2024.
2. Cléa Laouar, Yuki Takezawa, & Makoto Yamada. Large-scale similarity search with Optimal Transport. In EMNLP, 2023.
3. GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks, TKDE 2022.
4. Feature Robust Optimal Transport for High-dimensional Data, ECML 2022.
5. Post-selection inference with HSIC-Lasso, ICML 2021.