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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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systems increasingly provide personalized recommendations in domains such as nutrition and lifestyle. However, many recommender and prediction systems rely heavily on opaque machine learning techniques
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and Delft University of Technology. Curious to learn more about the project? Feel free to visit our website , where you’ll also find other exciting PhD opportunities related to this collaboration. In
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machine-learning algorithms, and with lightning-fast Maxwell solvers for scattering simulations. You will not only work on the 3-D models in theory; you will also be trained in operating advanced microscopy
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partners all over the Netherlands for a 4-year research position that bridges human-computer interaction, computer science, design, and behavior change. Information In the Netherlands, almost 300.000 people
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Electrical Engineering, Computer Science, or a related discipline. A research-oriented attitude. Solid background in machine learning and optimization methods. Knowledge and experience in (wireless
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Vacancies PhD Position - Developing methods for multi-disease test evaluation Key takeaways Background and Challenge In healthcare, numerous "multi-disease tests" are quickly appearing. They aim
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-physical systems secure and resilient in the presence of uncertainty and cyber-physical attacks? Then you may be our next PhD candidate in resilient and learning-based control of cyber-physical systems
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Researcher (R1) Application Deadline 8 May 2026 - 21:59 (UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 38.0 Is the job funded through the EU Research Framework
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, and/or machine learning. Preferably you finished a master in Computer Science, (Applied) Mathematics or related masters. Expertise in the field of visualization or visual analytics. You have good