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experience in machine learning and molecular simulation ? We're looking for our future PhD student ! Join us at Université Côte d'Azur, recognized since 2016 for its scientific and educational excellence
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techniques and novel simulation method. * Research Environment Support up to 10 million yen depending on the research plan at the time of arrival is available. For more information on the research field
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, nanobioscience, and organic chemistry. You can find more information here: https://researchprofiles.ku.dk/da/persons/knud-jørgen-jensen/ Tasks The laboratory assistant will be expected to assist the scientific
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design recovery and stability strategies using large-scale simulation workflows. Build and expand realistic, continent-scale power system models (e.g., the European transmission grid). Implement and test
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companies' transitions towards a sustainable society. In research and education, we work in a broad spectrum. From basic mechanical engineering via modelling and simulation towards innovative product-service
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Position Details Position Information Requisition Number Stu05001P Home Org Name RFID Lab at Auburn University Division Name College of Business Position Title RFID Lab: Simulation Team Software
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. Approximated gross salary: 43.537,90 EUR per year. Applications must include an electronic copy of the PhD certificate. Furthermore, in order to illustrate the applicant's match with the requirement and
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proceed with the definitionand numerical implementation of the models for the simulation of the magnetic, electrical, thermal, and mechanical behavior of these devices. Appropriate experimental setups will
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of configurations generated in silico as well as their thermodynamic, dynamic (phonon), and mechanical properties, and performing DFT/MLIP NpT and NVT molecular dynamics simulations. - Analyzing and exploiting
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Postdoctoral Research Associate will be responsible for AI-driven materials discovery. The position will collaborate with interdisciplinary teams to develop integrated molecular simulation and machine learning