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perception systems, using deep learning and simulation-to-real domain adaptation techniques. You will work with a multidisciplinary team, contributing to fundamental and applied research. Your role will
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-based participatory research, behavioral health, occupational health, environmental health, climate change, public health informatics, or structural and social determinants of health. Deep knowledge and
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Expertise: Familiarity with supervised/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g
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learning, or deep reinforcement learning; Hands-on experience with systems integration, including Robot Operating Systems; Enthusiasm to supervise UG/MSc/PhD students. Downloading a copy of our Job
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investment in artificial intelligence over the next five years (regjeringen.no ) (in Norwegian). About the project/work tasks: This PhD fellowship is associated with the third cluster of the AI LEARN centre
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paradigms centered on human perception. Finally, the recent rise of foundation models and multimodal artificial intelligence opens up new perspectives at the interface between coding and machine learning
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Physics with applications to Material discovery and quantum chemistry - Gen AI designing materials with target properties Astrophysics and cosmology: Deep learning for telescope image analysis
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/or spatial genomics, computational biology, machine learning, bioinformatics, and systems neuroscience. Prior experience with deep learning applied to biological data is a plus. Practical experience
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& Machine Learning • Clinical pathways and decision support for patients with acute chest pain • AutoPiX – Explainable Deep Learning for Multimodal and Longitudinal Imaging Biomarkers in Arthritis • Speaking
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that preserves object identity or style. They should have a solid publication record in top-tier computer vision conferences such as CVPR, ICCV, or ECCV, and demonstrate proficiency in deep learning frameworks