64 engineering-computation "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" scholarships at Forschungszentrum Jülich
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interdisciplinary team of engineers, computer scientists, and life scientists Regularly participate in international conferences to present your own work, and learn about state-of-the-art machine learning and
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Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis. You will
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edge of energy systems and computational engineering, developing scalable methods to simulate and secure IBR-dominated grids. Your key responsibilities include: Conducting large-scale simulations
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, earth sciences, energy systems, or material sciences University degree (M.Sc. or equivalent) in applied mathematics or in computational engineering science, computer science, simulation science with a
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Germany 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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grades in the field of mechanical engineering, material science, physics, computational science or similar, preferably with a specialization in the field of theory and/or simulation Strong understanding
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, energy systems, or material sciences A Masters degree with a strong academic background in mathematics, computer science, physics, material science, earth science, life science, engineering, or a related
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Germany 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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development of your personal strengths, e.g. through an extensive range of training courses; a structured program of continuing education and networking opportunities specifically for doctoral researchers via
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of data scientists, software engineers, and experimental researchers on topics including: Developing multi-scale and multi-modal representation learning methods for scientific imaging data (e.g., SEM