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efficient GPU-supported implementation. Your tasks: Development of physics-aware ML models for 3D blood-flow prediction Integration of domain decomposition methods into the learning framework to enable
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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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analysis systems using GPU- and FPGA-supported HPC clusters at large international research facilities such as Effelsberg, SKA, and MeerKAT. The systems developed by the BDG are based on state-of-the-art
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Max Planck Institute for Solar System Research, Göttingen | Gottingen, Niedersachsen | Germany | 2 days ago
with turbulence, flow control, scientific computing, or data-driven modeling. Experience with GPU computing and supercomputers is desirable. Good English skills. Additional information The position is
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model in collaboration with partner institutions such as the German Climate Computing Center (DKRZ) and German Weather Service (DWD), including GPU porting. They will perform production runs of ICON and
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to decompose the task into generating individual 2D horizontal layers separately in order to save GPU memory resources. Your Qualifications / Experience: completed MSc university degree in mathematics
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frameworks (e.g., PyTorch). Familiarity with GPU-accelerated environments, virtualization tools, and prototyping using real testbeds (e.g., SDR). We expect a diploma in computer science or telecommunication
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Max Planck Institute of Animal Behavior, Radolfzell / Konstanz | Konstanz, Baden W rttemberg | Germany | about 1 month ago
Behavior (CASCB), providing access to shared infrastructure, high-speed data networks, and central technical services. Computational needs will be met through multiple layers of resources: local GPU-equipped
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and GPU servers for the delivery of the PC exercises, and jointly supervising the PC exercises during the course What you contribute Student on a STEM degree programme Good knowledge of at least one of
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of spikes by a model Develop proxy apps representing the different processing stages of spiking network simulation code (targeting CPU and accelerators such as GPU or IPU) Systematic benchmarking of proxy