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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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, as reliable decisions must often be produced from partially observed data. Event-based sensing and neuromorphic computing offer promising paradigms to address this challenge by providing asynchronous
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) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission of teaching and
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systems Experience in deep learning, computer vision, or multimodal data integration Exposure to federated learning, privacy preserving analytics, or distributed systems Knowledge of clinical data models
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decisionmaking modules that must operate under strict constraints on latency, compute, energy, and reliability. In many robotic platforms (mobile robots, drones, autonomous vehicles, ...), inference must execute
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, spatial relationships, and contextual information. The framework would focus on real-time inference and investigate temporal search strategies using vision–language large models to identify anomalies in
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The European Synchrotron, the ESRF, is an international research centre based in Grenoble, France. Through its innovative engineering, pioneering scientific vision and a strong commitment from its
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unit (UMR 7248) from UCA and CNRS. Abstract Optical flow estimation is a key task in computer vision, particularly critical for autonomous navigation where accurate motion perception is essential. It can
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The candidate should preferably have a PhD in Computer Science or Robotics with a solid background on deep learning and 3D scene understanding. Experience with LiDAR and Computer Vision is a plus. The candidate
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Automated Generation of Digital Twins of Fractured Tibial Plateaus for Personalized Surgical plannin
in Computer Vision; 2009 Oct 12–16; Trégastel, France.Available from: https://inria.hal.science/inria-00404638v1/document 5. Micicoi G, Grasso F, Kley K, Favreau H, Khakha R, Ehlinger M, et al