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of the largest French national research institutes for fundamental research in condensed matter physics enriched by interdisciplinary activities at the interfaces with chemistry, engineering and biology. It
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the “Surfaces and Interfaces” department, as well as the associated experimental platform, under the supervision of Prof. O. Ersen. The expertise of the group lies in the investigation of materials and physico
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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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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
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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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been proposed: traditional computer‑algebra methods [3], reduction of the problem modulo a prime p [6, 2], and symbolic‑numeric methods [4, 1]. This postdoc proposal concerns the second modular approach
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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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. This project aims to evaluate its potential as a regulator of the antitumor immune response. The site is accessible by tram (Line A, Campus Illkirch station) from the Strasbourg train station or by car (parking