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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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Description CNRS offers a 18-month fixed-term contract researcher position to work on the recently funded project ACCTS (“Assessing cirrus cloud thinning strategies by learning from aerosol-cirrus interactions
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Research FieldPhysicsYears of Research ExperienceNone Research FieldTechnologyYears of Research ExperienceNone Additional Information Eligibility criteria - Hold a PhD in polymer chemistry - Have solid
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scienceYears of Research ExperienceNone Research FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria Degree : PhD in computer science, machine learning, or computational
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elucidating the molecular and cellular mechanisms of the late phase of long-term potentiation (LTP), a key process in learning and memory. The project is based on the development and use of an innovative
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combine density functional theory (DFT), molecular simulations, and machine-learning force field (ML-FF) development to uncover the factors controlling NHC–surface interactions and to model realistic
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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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learning simulations - Growth of metallic heterostructures by sputtering / ALD - Optical and e-beam lithography - Ion beam and reactive etching - Fabrication of skyrmion based nano-devices - Electrical
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conferences. • Contribute to the writing of scientific publications. Optional : • Design Machine Learning (ML) potentials. • Code in FORTRAN and PYTHON to improve the functionality of the global
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of Research ExperienceNone Additional Information Eligibility criteria Education - PhD in education, cognitive psychology, occupational psychology/ergonomics, educational technology, or a related field. - Dual