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frameworks to understand, predict, and control tissue-scale dynamics by integrating mechanistic and data-driven approaches across molecular, cellular, and tissue scales. Particular interest will be given
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localization and navigation. Experience in one or more of the following: path planning, motion planning, trajectory optimization or model predictive control; reinforcement learning, imitation learning
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 2 months ago
. Preferred qualifications include experience with: Optimal Control and/or Model Predictive Control (MPC). Modeling of deformable parts. Real-time numerical optimization. LanguagesFRENCHLevelBasic
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interconnected and closely intertwined scientific themes. The first targets the development of hybrid algorithms combining multi-physics modelling of electronic components, predictive control and machine learning
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expand and improve an existing modelling framework to predict direct and indirect nitrous oxide and methane emissions from agriculture. You contribute to the following activities: Performing a SWOT
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The candidate will develop and benchmark zero-shot multimodal fusion models for rare disease prediction, using melanoma as a use case. The project integrates spatial and single-cell multi-omics data with
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at the University of Rhode Island. URI has dedicated significant expertise and resources to develop a suite of tools, including the Coastal Hazards Analysis, Modeling and Prediction system (CHAMP), STORMTOOLS, and
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, observations, a hierarchy of numerical models, and machine-learning methods to understand their formation, dynamics, and predictability. The successful candidate will have substantial freedom to develop
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reinforcement learning, learning-based adaptation and control, physics-informed machine learning, learning-enhanced model predictive control, control-theoretic safety and stability guarantees for learning-enabled
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for understanding their reliability and for making informed decisions based on their predictions. This project aims to develop new methods for uncertainty quantification in mathematical and statistical models