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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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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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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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or similar deep learning frameworks GPU know-how: Familiar with GPU workflows and distributed training setups Data competence: Experience in preprocessing, augmentation, and dataset organization; confident
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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
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, Pandas, SQL, Docker, git, etc. PyTorch skills: experience in training machine learning models with one or more GPUs; ability to work with pre-existing codebases and get a training run going A versatile
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models on one or more GPUs and the ability to work with existing codebases to set up training runs Research interest in one or more of the following areas: probabilistic machine learning, time series