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, “Time-Varying Operator-Theoretic Framework for Tipping Point Prediction” (PI: Prof. Sho Shirasaka) in the JST PRESTO research area “Exploration of New Science Using Mathematics to Predict and Control
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on the vision of developing multi-level thrombosis risk prediction models, from cellular dynamics to organ-level hemodynamics. The network integratesin silico, in vitro, and in vivo approaches to understand
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these challenges by advancing sensitivity-based modelling, fluid–structure interaction (FSI) methods, inverse problem solving, and surrogate modeling techniques, ultimately enabling predictive, adaptive, and
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Position Information Position Information Working Title Manager, Biophysical Model Design (Temporary) Department Biochemistry-0831 Requisition Number S_260080 Posting Open Date 02/04/2026
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accepted all year round Details Dynamic optimization is integral to many aspects of science and engineering, commonly found in trajectory optimization, optimal control (e.g. model predictive control, MPC
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and the interplay with polymer viscosity. Structure-Property Relationships: Establishing the relationship between polymer flow, fibre displacement and the manufacturing parameters. Building a predictive
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AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
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University College of Medicine, our goal is to use the lens of metabolism to better understand and predict cancer progression. We use a combination of experimental and clinical data paired with computational
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regression to represent unknown dynamics for model predictive control. Despite the practical success, there are still many theoretical open questions regarding scalability, uncertainty bounds and deriving
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discipline. Demonstrated expertise in clinical and biomedical NLP, including both predictive modeling and generative applications using foundation models Hands on experience building end to end ML systems