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techniques. As a research assistant, you will work closely with PhD candidates and postdocs to operate this automated synthesis platform. You will support ongoing projects and be responsible
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, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will
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sciences, law, and philosophy. Four WPs address citizen-empowerment-scenarios (CES) in healthcare, mobility, public governance, and healthy living. Each PhD position is embedded in one work package and
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Mathematics (Inverse Problems), Computer Science (Machine Learning, Computer Vision, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of
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storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine
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. Methodological Approach Candidates will develop and apply state-of-the-art machine learning techniques, including deep learning, representation learning, variational autoencoders, and graph-based models. A strong
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is made for you! Information We invite highly motivated students with a strong background in mathematical control theory, and a keen interest in machine learning to apply for the PhD position within
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to effective human video perception. What you will do The PhD student is responsible for helping achieve the objectives outlined above. The ideal candidate for this position has a strong background in machine
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machine learning packages (e.g.PyTorch). Completed academic courses in AI or machine learning. Interest in societal, ethical and philosophical questions. We consider it an advantage if you bring one or more
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sizes and frequencies by: Measuring rock fractures from UAV data using manual and automated mapping approaches (e.g., machine learning, convolutional neural networks). Monitoring physical weathering