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of Research Experience1 - 4 Additional Information Eligibility criteria We are seeking a highly motivated and talented postdoctoral candidate interested in molecular simulations, deep learning, generative
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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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predictive models of immune responses. Develop advanced and innovative machine learning methodologies and analyze data. The postdoctoral researcher will join the groups of T. Mora and A. Walczak, whose
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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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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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through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description -This postdoctoral position is part of
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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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detection of hidden objects or structures, for instance in medical imaging and ground inspection. The postdoctoral researcher will work in close collaboration with researchers in charge of the machine
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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 Post Doctoral position is part of the SilentPitch ANR project which involves a puri-disciplinary team of researchers including machine learning, speech science, cognition and behavioural studies