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applicants should have a strong academic record with a solid background in Machine Learning. Knowledge of Vision-Language-Action models and Novel View Synthesis techniques is a strong plus. Good programming
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programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
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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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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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Deep Learning (CIDL), part of the Leiden Institute of Advanced Computer Science (LIACS). As a team, we develop cutting-edge techniques for advanced computational imaging systems, combining expertise from
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of Applied Math at the University of Twente has a diverse and vibrant environment for research in Machine Learning and adjoining areas, such as Deep Learning, Mathematical Statistics, Combinatorial
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across both surface and subsurface layers. This includes constructing robust feature extraction pipelines, attention-based fusion architectures, and deep learning models that accurately characterize cracks
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Conduct original and novel research in the field of Computer Vision and Machine Learning Develop and analyse novel deep-learning methods to learn for visual representation learning; Publish and present
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Internationalism in Africa (IRInA), led by Dr. Gulnaz Sibgatullina. The project examines how religious actors contribute to the emergence of alternative, non-liberal visions of global order, with a particular focus