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will perform experiments, develop dose-response models and mechanistic understanding allowing to develop a workflow allowing understanding and awareness amongst different stakeholders. You will be
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, optimization, control, game theory, and machine learning. Interdisciplinary by design: Work at the intersection of energy systems and markets, privacy and cybersecurity, forecasting, optimization, control, game
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that leverages the full spectrum of available data sources. The thesis should address the following questions: 1) How can one improve perception systems using data coming from different sources? 2) How
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: Collaborate with other PhD candidates and researchers working on the project to share insights and learn from its different sub-projects. The successful PhD candidate will be based at the Faculty of Industrial
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. Nice to have: Practical experience with machine-learning frameworks (e.g., PyTorch). Prior tape-out experience (ASIC or a complex FPGA prototype) and familiarity with the digital back-end flow (synthesis
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, disability studies, (co-)design methods, and Human-Computer Interaction (HCI). The ideal candidate is passionate about creating socially impactful inclusive co-design methods and eager to collaborate directly
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8 Sep 2025 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Computer hardware Computer science » Digital systems Engineering
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analyses and machine learning. Some data for the project already exist, but additional data will be collected from behavioural tests on privately owned pet dogs in Sweden and abroad (Europe). Travel and time
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? No Offer Description Job description Consortium This position is part of a European Doctoral Network consortium "Machine learning for integrated multi-parametric enzyme and bioprocess design", where 15
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such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting