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, PyTorch) for ML applications, training, evaluation, and deployment of models Use of GPU-based servers and modern IT infrastructure for training and inference Application of classical ML methods (e.g
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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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, 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
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- and multi-GPU setups, and ability to work with existing codebases to quickly get training pipelines running Deep research interest in one or more of the following areas: 3D Gaussian Splatting, Neural