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, or ability to learn quickly) and/or symbolic/numerical computation (Mathematica, R, Python). • Fluency in scientific English (reading, writing, speaking) is essential to interact with the project's
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-phase heterogeneous photocatalysis would be a plus - A first experience in analytics related to photocatalytic gas-phase reactions would also be a plus - Fluency in French or English, willingness to learn
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atmospheric perturbations, and improving performance under realistic operational conditions. Main activities include: • Designing and developing deep learning models to correct wavefront sensor nonlinearities
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condensed matter physics • Ability to learn and develop skills in analytical computation, theoretical modelling and numerical simulations, in particular the numerical solution of partial differential
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Deployment Strategies - Model Compression: Investigate techniques such as quantization, pruning, and knowledge distillation to reduce the computational and memory footprint of deep learning models without
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are looking for a highly motivated individual with experience in spectroscopy and analytical chemistry who enjoys experimental, analytical and modeling work. The candidate should have a sufficient background in
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: Marine Biodiversity and ecosystem functioning across spatial, temporal, and human scales”. The overall aim of the project is to acquire knowledge of the principles governing the structure, dynamics
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applied methodologies in Data and Image Analysis, Computational Imaging, Statistical Learning, Uncertainty Quantification, Robust Estimation, and Deep Neural Networks. The group combines expertise in
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. In this project, we aim to develop digital tools combining density functional theory (DFT) and machine learning (ML) to accelerate the in-silico design of solid catalysts for the DA process. - Perform
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elucidating the molecular and cellular mechanisms of the late phase of long-term potentiation (LTP), a key process in learning and memory. The project is based on the development and use of an innovative