研究ユニット

Four researchers talking in a lab

准教授の公募

沖縄科学技術大学院大学(OIST)は、日本にある国際的・学際的な大学院大学で、教育と研究はすべて英語で行われています。世界トップクラスの研究者が革新的な研究成果を生み出せるよう、研究の自由度と充実した研究環境、幅広い支援体制を提供しています。現在、独立した研究室の主宰となる、テニュアトラック准教授(Assistant Professor)を募集しています。

  • 化学および材料科学(物理科学を含む広範な分野)
  • 生命科学および神経科学(理論的アプローチおよびAIを含む)
     

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Blue strings spreding like a tree

モデルベース進化ゲノミクスユニット

The Model-Based Evolutionary Genomics Unit works at the crossroads of computational and evolutionary biology. Our long-term goal is to achieve an integrative understanding of the evolution of Life on Earth and the origins and emergence of complexity across different biological scales, from individual proteins to ecosystems. To move towards this goal, we develop and apply model-driven evolutionary genomics methods to reconstruct the Tree of Life and the major evolutionary transitions that have occurred along its branches.
Gergely János Szöllősi profile photo

ソローシ・ゲルゲイ・ヤーノシュ

准教授

Biological Nonlinear Dynamics Data Science Unit

生物の非線形力学データサイエンス研究ユニット

The biological nonlinear dynamics data science unit investigates complex systems explicitly taking into account the role of time. We do this by instead of averaging occurrences using their statistics, we treat observations as frames of a movie and if patterns reoccur then we can use their behaviors in the past to predict their future. In most cases the systems that we study are part of complex networks of interactions and cover multiple scales. These include but are not limited to systems neuroscience, gene expression, posttranscriptional regulatory processes, to ecology, but also include societal and economic systems that have complex interdependencies. The processes that we are most interested in are those where the data has a particular geometry known as low dimensional manifolds. These are geometrical objects generated from embeddings of data that allows us to predict their future behaviors, investigate causal relationships, find if a system is becoming unstable, find early warning signs of critical transitions or catastrophes and more. Our computational approaches are based on tools that have their origin in the generalized Takens theorem, and are collectively known as empirical dynamic modeling (EDM). As a lab we are both a wet and dry lab where we design wet lab experiments that maximize the capabilities of our mathematical methods. The results from this data driven science approach then allows us to generate mechanistic hypotheses that can be again tested experimentally for empirical confirmation. This approach merges traditional hypothesis driven science and the more modern Data driven science approaches into a single virtuous cycle of discovery.
Gerald Pao

ジェラルド・パオ

准教授

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