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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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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Your tasks: Developing optimization algorithms for massively parallel hardware architectures such as AI
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Application Deadline 27 Jan 2026 - 23:59 (Europe/Berlin) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Other EU programme Is the Job related to
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Framework Programme? Horizon Europe - MSCA Marie Curie Grant Agreement Number 101226599 Is the Job related to staff position within a Research Infrastructure? No Offer Description Overview of the project and
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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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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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. Your Profile: A Masters degreee with a strong academic background in physics, mathematics, computer science, or a related field Proficiency in at least one programming language (Python, C
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Germany Application Deadline 28 Nov 2025 - 22:59 (UTC) Type of Contract To be defined Job Status Other Offer Starting Date 30 Oct 2025 Is the job funded through the EU Research Framework Programme? Not
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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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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