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Bayesian statistics, stochastic modeling, and optimization under uncertainty • Proficiency in Python programming • Strong interest in applied and transferable research • Knowledge of industrial simulation
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Information Eligibility criteria * Skills/knowledge: statistical physics, physical chemistry, machine learning * Expertise: molecular simulations, programming experience (Python, C++, Julia, C, etc.), Python
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ranking may be requested). The position requires very solid theoretical knowledge in organic chemistry, molecular catalysis and materials science (syntheses and characterizations). Knowledge in
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, preferably with experience in using tensor networks for condensed matter systems. Desired skills: - Knowledge of tensor networks. Experience with their application to two-dimensional systems will be valued
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portability of the proposed solutions. - You will integrate and deliver "development kits" to the application community. - You will ensure knowledge transfer by providing support and training. - You will be
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LanguagesFRENCHLevelBasic Research FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria Excellent knowledge of large-scale comparative genomics (i.e., at the archaeal level) and
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or she must demonstrate strong motivation for research at the chemistry-biology interface, good organizational skills, and autonomy. Knowledge of glyco-chemistry would be an advantage. Website
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to: • Possess solid knowledge in biochemistry, particularly in the production (cell-based and cell-free systems) and purification (by chromatography) of proteins and biomolecules, in enzymology, as well as in
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synthesis. In particular, we aim to develop process knowledge bases to support generative methods and, secondly, to develop a tool for determining the optimal structure of any type of thermodynamic cycle by
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knowledge about the relationship between the implementation of thermoplastic composites and their behavior under extreme conditions. They will also enable recommendations to be made for the manufacture of low