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mathematical foundations needed to make deep operators reliable, robust, and applicable for control of complex engineering systems. In this PhD project, you will investigate how operator-learning models can
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: to develop next-generation autonomous crop monitoring and decision-support systems for Controlled Environment Agriculture. By integrating plant sensing, data and crop models, we aim to enable more precise and
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, and Posit Connect for deploying dashboards and predictive models that support the University's fundraising campaigns. This position reports directly to the Senior Director of Advancement Analytics in
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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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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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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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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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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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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