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degree in computer science, mathematics, statistics, physics or relevant fields. Strong background in machine learning, preferably experience in probabilistic modeling, Bayesian machine learning, or graph
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on developing and studying privacy-preserving methods, such as differential privacy, Bayesian privacy, federated learning and synthetic data. The aim is to enable meaningful analyses, such as identifying disease
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can be used as a practical tool to identify and adjust recipes that, for a given machine and processing window, fulfil defined target criteria. Building on existing evidence that Machine Learning can
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, climate neutrality targets, or regenerative ambitions. Against this background, BUILD invites applications for one fully funded PhD position within absolute environmental sustainability assessment