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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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commonly used on Unix systems. Additional languages or experience with libraries for utilizing GPU hardware efficiently, e.g., CUDA, are a plus. Experience in AI programming with, e.g., PyTorch(-DDP
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optimization layers Increase inference efficiency (e.g., GPU acceleration) and assess the applicability domain of learned algorithms Publish and present your results in peer-reviewed journals and at
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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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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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construction is being developed. It enables architects and planners to implement building designs significantly faster and more efficiently by combining generative design, automated floor plan creation
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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
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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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learning experiments Conduct applied research on exciting projects Prepare presentations and papers Test software and frameworks What you contribute You are currently enrolled in a master’s program in
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Testing software and frameworks for 3D reconstruction pipelines What you contribute Enrolled in a master’s program (Berlin/Brandenburg) in computer science, mathematics, statistics, physics, electrical