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Your Job Develop a reinforcement learning (RL) controller for a liquid–liquid gravity settler, trained entirely offline in a simulated environment Use existing physics-informed neural network (PINN
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consumption on complex algorithmic or cognitive tasks. This project is part of the ELEVATE MSCA Doctoral Network (https://www.elevate-dn.eu/) and co-supervised by our partners at the university of
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control water uptake investigate hydraulic adaptation to drought analyse the role of stomatal regulation in maintaining plant water status You will work with modelling approaches implemented in
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and applying optimisation methods, such as model predictive control, multi-objective optimisation and, where appropriate, reinforcement learning Investigating suitable model-reduction and approximation
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such as SPM, FSL, FreeSurfer, AFNI, MRtrix, CONN, or similar tools is desirable Knowledge of neuroimaging preprocessing, quality control, graph theory, connectomics, multivariate statistics, or
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proximity to the quantum processor, is necessary to control and detect quantum states more quickly and efficiently, while limiting the wiring to ambient temperature instrumentation [1,2]. Other