192 parallel-and-distributed-computing-"Multiple" positions at Technical University of Denmark in Denmark
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passionate about working in a team. You will lead the publication of results in high-impact scientific journals. You have a strong background in Physics, Geophysics, Materials Science, Computation, Engineering
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samples, by the development of advanced computational multiphysics models of stress corrosion cracking and coupling these with process-microstructure models (being developed within MicroAM project). Main
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. The ideal candidate will have a strong background in Physics, Geophysics, Materials Science, Computation, Engineering, or a related discipline. Documented research experience in one or more of the following
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general planning of the PhD study programme, please see DTU's rules for the PhD education . We offer DTU is a leading technical university globally recognized for the excellence of its research, education
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candidate must also fulfill the requirements for admission to a PhD program at DTU. You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two
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to your skill set and interests – related to building infrastructure and enabling easy access to shared datasets and moving them to the compute resources. The goal is to assist users in accessing and
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, or reinforcement learning Proficiency in Python and contemporary software ecosystem for optimization, dada management, and API development Experience with teaching and supervision Experience with IoT, edge computing
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nanoparticles and reactions at the atomic-level by combining path-breaking advances in electron microscopy, microfabricated nanoreactors, nanoparticle synthesis and computational modelling. The radical new
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our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by Prof. Henning
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: Background in Data Science, Computer Science or related fields; Working experience in implementing AI models (not just loading pre-trained model). PyTorch framework is preferred; Experience with APIs