28 parallel-computing-numerical-methods-"https:" research jobs at Forschungszentrum Jülich
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with multicomponent composition. You will play a pivotal role in this undertaking, developing core quantum-computing methods and methodical interfaces for integration into a larger framework of quantum
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willingness to learn: High-performance computing (distributed systems, profiling, performance optimization), Training large AI models (PyTorch/JAX/TensorFlow, parallelization, mixed precision), Data analysis
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, Statistical Physics, Genome Annotation, and/or related fields Practical experience with High Performance Computing Systems as well as parallel/distributed programming Very good command of written and spoken
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theoretical models and methods as well as in implementing numerical optimization techniques Interest in working closely with experimentalists Detailed knowledge of quantum physics and experience with quantum
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-edge Machine Learning applications on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be
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on the Exascale computer JUPITER. Your work will include: Developing, implementing, and refining ML techniques suited for the largest scale Parallelizing model training and optimizing the execution User support in
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) Exchange and close collaboration with partners from physics, computer science, and social psychology Your Profile: Completed master`s degree followed by a doctorate in physics, computer science, mathematics
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, computer science, and social psychology Your Profile: Completed master`s degree followed by a doctorate in psychology, cognitive science or in a similar field of study Research experience in the areas of crowds
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therapeutics by protein design. This project will apply cutting-edge generative AI methods—including protein design, structure–function prediction, and multimodal learning—to develop and optimize a new