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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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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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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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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
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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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applications for a two-year postdoctoral position on the topic “War and Peace.” This topic has been identified by CNRS SHS in its foresight exercise as a scientific priority (https://www.inshs.cnrs.fr/fr
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the supervision of students (interns and/or PhD candidates) -preparation of scientific publications and progress reports -presentation of results at national and international conferences This work will be carried
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part of the IPHC's Department of Analytical Sciences (UMR7178, CNRS / Université de Strasbourg), and comprises around fifty permanent and non-permanent staff (researchers, engineers, PhD students
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studies. The position depends little on the volume of collected data: its validation relies on formal analysis, ablations and targeted studies. Where to apply Website https://emploi.cnrs.fr/Offres/CDD
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to publicly available data, in particular from DESI/Euclid and the Fermi-LAT telescope. Qualifications: Applicants should have: a PhD in cosmology or astrophysics (completed by the start date), demonstrated