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theory-driven models of cognition and neural activities and data-driven predictive modeling for precision medicine. In addition to our core research questions and mission, the unit will also serve as a
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surface modelling; understanding of engineering drawing and design documentation practices; knowledge of dimensional chains, tolerances, fits, and geometrical tolerancing; ability to read and analyse
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technologies and knowledge exchange, leveraging the power of disease modelling to improve human health. Responsibilities: The Scientific Director will act as the legal representative and the executive
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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of these basins. A depositional model for the Cenozoic sequences of central Cotentin describes a succession of Atlantic-origin transgressions—transporting shelly sands and gravels-occurring within a context of
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, SimpleITK, MONAI, or nibabel), knowledge of Linux, Git, virtual environments and containers (Docker), and experience in training models on GPU in secure environments are required. Languages: Oral and written
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language models and knowledge representations of knowledge graphs under the Pytorch environment are expected. Target data are written in English. Where to apply Website https://jrecin.jst.go.jp/seek
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-level abstraction models aims at: preserving supplier confidentiality speeding up simulations compared to RTL/netlist approaches incorporating power and thermal estimation mechanisms for heterogeneous
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research-related duties as assigned. Required Qualifications Proficiency in coding and programming. Extensive knowledge of Large Language Models (LLMs). Experience using LLMs for data extraction or text
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/science.aea5113) Stockis J et al. Science Immunology 2024 (DOI: 10.1126/sciimmunol.adl1903) Schuijs MJ et al. Nature Immunology 2020 (https://doi.org/10.1038/s41590-020-0745-y ) Preferred skills/knowledge Cell