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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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-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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PhD position: Global soil mapping with process-informed machine learning Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline
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degree in AI, Computing Science, Mathematics, or Data Science. Strong coding, communication and organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch
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of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PhD candidate who is eager to work on AI based solutions for predictive inteligence for MRI scanning. The candidate will