57 computer-science-intern "https:" "https:" "https:" "https:" "P" PhD positions at Forschungszentrum Jülich
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work Your Profile: Completed university degree (Master`s) in a subject with a strong focus on chemical engineering, e.g. process engineering, mechanical engineering, technical chemistry, relevant
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in the 36 months immediately before your recruitment date (mobility rules of the Marie Skłodowska-Curie program) Masters degree in electrical/electronic engineering, computer engineering, computer science
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electrical engineering, computational engineering, or a related discipline A strong foundation in power system modelling and simulation Solid programming skills (Python, C++, or comparable languages) Interest
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, including statistical tools and programming, to large multidimensional datasets from chamber and airborne experiments Present your results at international conferences and publish them in peer-reviewed
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international project partners, embedded in an ERC-funded project. For deeper insights, please have a look at our institute website: https://www.fz-juelich.de/en/pgi/pgi-7 and the research group in which
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Computer Science, Mathematics, Physics or a related field Knowledge in Neuromorphic Computing or Computational Neuroscience is a plus Experience in mathematical modelling Excellent programming skills and experience
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and individually, for example through training opportunities and the structured JuDocS program for doctoral candidates: https://www.fz-juelich.de/en/judocs In addition to exciting tasks and a
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Infrastructure? No Offer Description Work group: PGI-14 - Neuromorphic Compute Nodes Area of research: PHD Thesis Job description: Your Job: You will participate in an international team in an EU-funded Doctoral
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international research contexts Your Profile: Excellent Master’s degree in mechanical engineering, energy systems, computational engineering, or a related field Strong background in numerical methods and applied
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models of structural disorder based on the periodic average structure, crystal chemistry, and complementary information Generation of a dataset to train ML models Simulation of the corresponding