10 parallel-"https:"-"UNIVERSITY-OF-CANTERBURY" positions at Forschungszentrum Jülich
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) Expertise in further programming languages (in particular C++), GPU programming, parallel programming or high-performance computing are highly valued Keen interest in neuroscience is essential Experience with
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measurements, and simulation results Integrate data engineering workflows with HPC-based simulation environments Optimize and parallelize software solutions for execution on one of the world’s most powerful and
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Approach ( https://vhrz669.hrz.uni-marburg.de/ssf/ ). Understanding the mechanisms controlling subsurface flow (SSF) and the conditions under which it occurs remains a major challenge in hydrology and
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-aware learning methods with domain decomposition techniques, enabling parallel training and efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow
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GPU-capable, parallelized simulation frameworks. Work closely with experts in HPC and power systems to enhance scalability and computational performance. Disseminate your findings through scientific
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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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strong background in applied mathematics Excellent programming skills (Python, C/C++) Good experience in machine learning and parallel computing Good organisational skills and ability to work both
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engineering Very strong mathematical and algorithmic background Programming experience (Python, C++, etc.) Familiarity with parallel programming frameworks (e.g. MPI, CUDA) Fluent in written and spoken English
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related in space and time and to behavioral events. Core Tasks: Getting familiar with the experimental data and the concepts of neuronal coding, and Elephant Analysis of the parallel rate data for
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data science courses, soft skill courses and annual retreats) https://www.hds-lee.de/about/ 30 days of annual leave (depending on agreed working time arrangements) and provision for days off between