126 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" positions in Luxembourg
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The successful candidates will join the Computer Vision, Machine Intelligence and Imaging (CVI2) research group, led by Prof. Djamila Aouada, to conduct research in Artificial Intelligence with a
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generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research directions and
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The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. To be at the forefront of innovation in teaching and learning
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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Investigator of the project, Dr. Andre Groeger, together with work package-specific collaborators. The position offers unique learning opportunities, competitive economic conditions and the chance to participate
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Swarm Intelligence, Reinforcement Learning and Optimization Techniques. As a Postdoctoral researcher, you will: Lead cutting edge research in Swarm Intelligence and Machine Learning, addressing challenges
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Opportunities to work on innovative projects and network globally More information is available at: https://marie-sklodowska-curie-actions.ec.europa.eu/calls/msca-postdoctoral-fellowships-2026
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The Research and Development Specialistwill be a member of the Department of Geography and Spatial Planning (https://dgeo.uni.lu ), joining the Economic Geography team of Prof. Christian Schulz
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-performance computing resources suitable for large-scale machine-learning and foundation-model experiments. Your role We are seeking a highly motivated Postdoctoral Researcher to join the FNR AI-HPC 2025
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and