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Eligibility criteria Selection will be based on the following scientific and technical criteria: • PhD in computational biology, machine learning, bioinformatics or a related field. • Proficiency with Python
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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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, 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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-VIS-NIR). Experience in hyperspectral data processing (HMSPL, μXRF, μXAS) and statistical analysis (clustering, machine learning) is preferred. Familiarity with fossilization processes and taphonomic
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numbers effects beyond what is currently possible. • Perform mathematical and numerical analysis of tensor network methods. • Work on GPU implementation of tensor network solvers. • Disseminate research
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of Research Experience1 - 4 Additional Information Eligibility criteria We are looking for a doctor in particle physics with less than two years of experience after the PhD. Experience in machine learning and
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, ideally molecular dynamics and/or DFT. Scientific programming skills, particularly in Python, are expected. Familiarity with machine learning or generative AI methods applied to materials would be a strong
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. Development and integration of state-of-the-art machine learning techniques in the analysis and event reconstruction will be a major component of this work. - Characterization of silicon detection modules using
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physics and quantum chromodynamics. Knowledge of computer programming is a plus. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR3681-CAMFLO-002/Default.aspx Work Location(s) Number