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, machine learning techniques, etc.) is desirable. This thesis offer within the AstroParticle and Cosmology Laboratory (APC) is part of the Deep Underground Neutrino Experiment (DUNE). DUNE is an
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, CADENA seeks to replace toxic catalysts with eco-friendly, high-performance alternatives, aligning with France's and Europe's green chemistry and circular economy goals. Where to apply Website https
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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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the knowledge acquired during the PhD with team members and acquire new knowledge. - Engage with the Local team at LIPN and the wider national community working on proof theory, programming languages and
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reaction. This study will be carried out in collaboration with the CEA/IRFU and GANIL. The study focuses on proposals accepted at GANIL (https://www.ganil-spiral2.eu/ ) and Legnaro (https://www . lnl.infn.it
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MPC combining continuous controls (curtailment, storage) and discrete topology switching. • Develop scalable solvers and heuristics (relaxations, decomposition, learning-assisted policies) for near-real
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) and a global ocean model (NEMO). The selected candidate will contribute to the ANR-AIAI project (https://anr-aiai.github.io ). Scientific Context The melting of Antarctic ice shelves by the ocean is a
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remain poorly understood. Their structural heterogeneity and chemical complexity make accurate atomistic modeling particularly challenging. Recent advances in machine learning approaches provide a powerful
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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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objective will be to determine whether such exposure generates toxic effects and/or triggers the epithelial-to-mesenchymal transition (EMT), a process by which sedentary epithelial cells acquire invasive