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to disease prediction and prevention. Through our Team Science approach, we have achieved breakthrough discoveries in five key areas: neuromodulation (reinforcement learning), critical period mechanisms
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fps. We are also developing three-dimensional force-mapping techniques to measure the nanomechanical properties of biomolecules at the submolecular level. Recent publications: https://researchmap.jp
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Design-Build-Test-Learn (DBTL) cycle that rapidly iterates the design, synthesis, and evaluation of molecular-robot components. https://molbot-ex.org/index.html [Work content and job description] The E01
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boosting, and related machine-learning approaches Evaluation of model generalizability using train/test splits, leave-one-cohort-out validation, and leave-one-platform-out validation Assessment of predictive
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position is available for the research on theoretical and numerical methods related to any of the following: open quantum systems, quantum information processing and quantum machine learning, nonlinear
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metabolism in ageing models. The use of AI and Deep Learning for data analysis and the establishment of in vitro ageing experimental systems and factor isolation using CRISPR Screening are also planned