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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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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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imaging setups, XRF scanners, micro-XRF, and synchrotron XANES), as well as guidance at the intersection of paleontology, biology, and physical chemistry. Where to apply Website https://emploi.cnrs.fr
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https://emploi.cnrs.fr/Offres/CDD/UPR4301-LAELEG-068/Default.aspx Requirements Research FieldChemistryEducation LevelPhD or equivalent Research FieldBiological sciencesEducation LevelPhD or equivalent
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computational models and machine learning methods, as well as experience in repertoire data analysis. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR8023-CLAMAR-001/Default.aspx Work
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models capable of inferring the IPLS directly from widely available genomic and epigenomic data. Broadly, the project aims to develop integrative approaches at the interface of (epi)genomics, machine
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, statistics and scientific computing. * Proficiency in a scientific programming language, particularly Python, and the ability to develop reproducible processing procedures. * Knowledge of machine learning and
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, GC-MS/MS, and advanced NMR approaches. - **Activity 4:** Multivariate statistics and machine learning to identify microbial and chemical biomarkers of resilience and reveal the interactions linking
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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
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hold a PhD in computer science or mathematics, with computer algebra as a specialty. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7161-GOVVAN-019/Default.aspx Work Location(s