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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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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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on model behavior. We will divide our work into three thrusts: Thrust A: A first major objective will be to augment classical spike train analysis methods particularly those developed by Prof. Grün and
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 25 days ago
Mellon University, the Max Planck Institute for Astrophysics (MPA), and further institutions, have embarked on an ambitious program to improve our constraints on the initial conditions of the Universe
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program generation and optimization Verification, testing and security of software systems Explainability of AI and of software engineering Software for distributed, highly-parallel AI systems Intelligent
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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
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parallel, they will oversee the development and implementation of computational strategies to support and enhance research activities across the institute. Your Responsibilities: Lead and further develop
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Positions Country Germany Application Deadline 31 Dec 2025 - 23:59 (Europe/Berlin) Type of Contract Permanent Job Status Full-time Is the job funded through the EU Research Framework Programme? Other EU
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, parallel processing and hardware-oriented applications and optimisations as well as operating systems. The project will be carried out in close cooperation with Audi in Ingolstadt and Neckarsulm, aiming