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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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Dovrolis: PEAKS: Selecting Key Training Examples Incrementally via Prediction Error Anchored by Kernel Similarity. ICML 2025 Job requirements Master’s degree in: Computer Science, Machine Learning
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Vacancies 2x PhD positions in the Mathematical Foundations of Machine Learning on Graphs and Networks Key takeaways The Discrete Mathematics and Mathematical Programming (DMMP) group
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supervision signals (e.g., labels in a downstream task or symbolic constraints). You will perform machine learning research, developing a framework for learning interpretable and robust concepts with
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PhD position ‘Courage to Correct: Balancing Error Prevention and Learning in Strategic Crisis Teams’
Vacancies PhD position ‘Courage to Correct: Balancing Error Prevention and Learning in Strategic Crisis Teams’ Key takeaways In high-stakes crises, strategic teams often aim to avoid errors at all
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PhD candidate on Interprofessional Learning and Team Resilience through the Electronic Health Record
13 Mar 2026 Job Information Organisation/Company Maastricht University (UM) Research Field Educational sciences » Education Educational sciences » Learning studies Researcher Profile First Stage
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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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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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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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methods for evaluating intelligence in people are not suitable for AI and vice versa due to inherent differences in learning, memory, and processing between these systems. This project develops