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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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) and/or machine learning (about 10 PIs). The Physics Laboratory is about 180-member strong and conducts world-leading research on a broad range of topics, including quantum technology, statistical
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Eligibility criteria The recruited person must have expertise in cosmology, numerical development and machine learning. They must be proficient in the Python programming language, with experience in JAX being a
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, the ability to analyze the full dataset collected by the experiment will be severely limited. The L2IT is a leader in developing new track reconstruction algorithms using geometric deep learning methods
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- Geometry, Learning, Information and Algorithms - Speech and Cognition The Gipsa-lab comprises 150 permanent staff and approximately 250 non-permanent staff (doctoral students, post-doctoral researchers
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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mobile base, an arm, a gripper, a learned policy, a safety module). Each agent runs its own specialized solver and is coordinated to a common, dynamically feasible plan through distributed optimization and
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, integrating statistical inference, machine learning, and population genetics. We will develop advanced computational methods to characterize the functioning of T- and B-cell repertoires. The goal is to build
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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language processing that address concrete problems and are both theoretically rigorous and interpretable. The PhD is funded by the ERC CoG PANDORA (Deep Multimodal Learning for Mining and Generation of Arguments