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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
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and erosion for 60 years. One of the main objectives is to acquire fundamental knowledge about the processes controlling environmental risks related to the dynamics of metal contaminants (speciation
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in