212 machine-learning-"https:" "https:" "https:" "UCL" "UCL" scholarships in Netherlands
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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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Mathematics (Inverse Problems), Computer Science (Machine learning, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of the NXTGen High-tech
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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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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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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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16 Jan 2026 Job Information Organisation/Company Eindhoven University of Technology (TU/e) Research Field Computer science » Computer architecture Computer science » Computer hardware Researcher
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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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that addresses key societal challenges. Our faculty is large enough to make an impact on both a national and international scale, yet small enough to offer a personal and engaging learning experience. In
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(BCW) Experimental Psychopathology and Treatment (EPT) Learning, Education, and Development (LED) Work, Health and Performance (WHP) In this document , you will find specific information about the four
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discipline. Experience with deep learning framework PyTorch or similar. Strong background in machine learning, image or signal processing. Knowledge of SotA models for multi-modality and scene understanding