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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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tasks: You will work together with renowned astrophysicists and computer scientists in the DFG-funded “Dynaverse” Excellence Cluster You will invent, implement, and benchmark novel AI tools (reinforcement
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Your Job: Investigate the potential of novel computing architectures for lattice field theory workloads Contribute to the design and implementation of an open-source lattice field theory framework
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integrate linear and circular processes, enabling used products to be transformed into new generations. What you will do Implement GPU-accelerated Gaussian Mixture Model (GMM) learning in PyTorch Optimize
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with the domain of optical material behavior acquisition at a decent pace. What you bring to the table Very good C++ programming skills GPU & Shader programming, ideally knowledge of PBR (Physically
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to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design‑space exploration, and on‑line operational optimization of power systems
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./M.Sc.) in Computer Science, Machine Learning, Data Science, or a related field at a university in Berlin or Brandenburg Python expert: Solid knowledge of Python and practical experience with PyTorch
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for working collaboratively within an interdisciplinary team, pushing the boundaries in remote sensing, CV, ML, and neuroscience Enrolled in a master's programme of computer science, mathematics, statistics
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the necessary algorithms. You will also develop and document a graphical user interface which handles large processing tasks efficiently and uses multiprocessing and GPU acceleration where necessary. At the same
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interface which handles large processing tasks efficiently and uses multiprocessing and GPU acceleration where necessary. At the same time, the software must be lean enough to run not only on powerful