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the project “Modeling of optically excited states in periodic and molecular systems with the self-consistent Green's function methods” financed by the NAWA Returns grant awarded by Polish National Agency
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models for polymer-based materials. This project aims to leverage
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data in the context of multigenerational families. · (Co-)developing predictive models based on very large registry, medical, social/environmental, and molecular data, aimed at predicting health
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-Service (MaaS) ecosystem. The work will integrate deep reinforcement learning, autonomous agent modelling, and multi-objective optimization to enable predictive simulation, real-time resource management
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phenomena at stake and the accurate prediction of such complex multi-phase, multi-physics system is then necessary to optimize the system parameters entirely. The first step consists in designing and
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partner from data sciences provides data management and AI based Image analysis, an internal simulations group working on quantitative models to reproduce and predict experimental data, and an internal
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primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or
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in python programming and machine learning models for molecular discovery and chemical property prediction. For informal enquiries, please contact Dr Martin-Martinez at francisco.martin-martinez
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Materials Research Center (SusMat-RC) at UM6P. The successful candidate will work on an exciting project focused on extracting and analyzing experimental and computational data to develop predictive models
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innovative EU-funded project at the intersection of polymer chemistry, computational modelling, and machine learning. The primary role is to develop a complete in silico framework to accelerate the discovery