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19 Sep 2026 Job Information Organisation/Company CNRS Department Centre de Biophysique Moléculaire Research Field Chemistry Biological sciences Pharmacological sciences Researcher Profile Recognised Researcher (R2) Application Deadline 9 Oct 2026 - 23:59 (UTC) Country France Type of...
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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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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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renewable postdoctoral position is part of an innovative project focused on characterizing the modification of bio-based materials using natural enzyme mimics capable of oxidizing biomass, particularly
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collaborations to implement cosmological analyses derived from the observation of Type Ia Supernovae. This work, carried out within the framework of the ANR SCINF project, aims to develop inference methods based
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experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in particular simulation-based inference), strong programming
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architecture beyond the CMOS-based scheme and suitable energy-efficient hardware for the unconventional scheme is highly desired for future highly-integrated AI hardware on a chip. The French team at IEMN has
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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the channels
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paleoenvironmental and taxonomic reconstructions and optimize future collection strategies. The position is based at IPANEMA, a highly interdisciplinary European research platform exclusively dedicated to the study of
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sites, and potential storage performance. The recruited researcher will develop and test rapid screening strategies for nanoporous carbons, based on structures numerically generated within the team