52 parallel-and-distributed-computing-"Meta" "Meta" PhD positions at Forschungszentrum Jülich
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-based processing. This project will investigate event-driven learning approaches in the context of RL in an event-triggered fashion. Data efficiency will be improved by using meta-learning and pre
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on soil organic matter cycling Literature research and (meta-)analysis to provide evidence-based knowledge for model parameterization Quantify the coupled carbon and energy turnover of specific biomolecules
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equipment Opportunity to work in the highly interdisciplinary and exciting field of neuromorphic and brain-inspired computing, collaborating with physicists, neuroscientists, materials scientists, and
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. You will employ the trypanosome model established in our group to study its swimming behavior in soft tissue-like surroundings. This project is a part of the DFG-SPP 2332 priority program “Physics
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in geophysics, physics, geoscience, computational geoscience, or related natural sciences with an overall grade of at least good Experience in programming (e.g., matlab, phyton, C/C++) and parallel
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pair distribution function (PDF) data for structure refinement and modelling of heterogeneous catalysts Publication of results in peer-reviewed scientific journals and presentation at conferences Your
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promising lean alloy system for additive manufacturing, as the mechanical properties can be tailored through phase composition, distribution and morphology by tuning process parameters. The work is carried
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(UTC) Type of Contract To be defined Job Status Other Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research
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Devices - Photovoltaics) and high-performance computation (IET-3: Institute of Energy Technologies - Theory and Computation) towards the overarching aim of implementing an inverse design approach for novel
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: A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep