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, background in black-box optimization and machine learning are a plus. Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7606-CARDOE-006/Default.aspx Work Location(s) Number of offers
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techniques. Knowledge of quantum computing algorithms would be a huge benefit, as would knowledge of methods to model noise in qubit systems. The person should be willing to learn to evaluating physical
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simulation and/or machine-learning is required, as well as a good knowledge of associated theoretical tools (statistical physics of liquids, ...; programming experience among: Python, Fortran, C, C++, ...). A
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. Previous experience with electron holography is not necessary but candidates should be skilled in quantitative electron microscopy or in situ techniques and willing to learn. Knowledge of the physics
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particular their detection using at least one of the above methods. - Languages and coding: python (essential), good background in Computer Sciences (expertise in AI/deep learning welcome). Values: enthusiasm
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researcher in a SHS laboratory, deepen his/her knowledge in a specific field of research and acquire greater independence in the scientific field of SHS. The post-doctorate is being carried out in the context
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machine learning model on traffic features. And yet, these models have not made the transition to practice at ISPs. While inference models have shown to be accurate, their adoption has been slowed by