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Field
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knowledge of computational modeling and simulation (electro-thermal-mechanical systems) Experience with optimization algorithms and design automation An interest in battery systems, powertrain architectures
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environments like health care and environmental monitoring. This PhD project aims to address these challenges by exploring how evolutionary algorithms and reinforcement learning (RL) techniques can be combined
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interpreted by regression and tree-based machine learning algorithms to obtain even better mutants and develop mechanistic hypotheses. Various collaborations with ON-TRACT network partners across Europe allow a
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the imaging system for generating image datasets from agricultural machines Develop and implement computer vision algorithms for plant disease detection and health estimation in soybeans Design and
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flow reconstruction, enabling both real-time coarse diagnostics and high-fidelity offline velocity field estimation. Developing reinforcement learning (RL) algorithms for a multi-agent robotics system
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As drones and air taxis enter cities, understanding and
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management of microgrids, battery scheduling, and handling uncertain renewable generation without relying on forecasted data [1]-[5]. Studies reveal that parametric tuning of RL algorithms such as state and
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treatments for mental illness. To this end, we bridge computational models that target various levels of analysis, including the algorithms (e.g., reinforcement learning models) and their neural
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. You will be working at the intersection of battery modelling and algorithm development, with a strong emphasis on the data-driven modelling and control aspects. You will contribute to shaping
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, led by Orange Labs, whose objective is to design and implement efficient algorithms for computing optimal solutions in order to recommend the best actions a telecommunication operator can undertake for