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skills (Python, R, Java, …) and interest to work in polyglot software environments Practical experience with machine learning and AI methods and an interest to learn, adapt and apply ML methods
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models, multi-view computer vision, semantic graph-based representations, and self-supervised learning—to automatically interpret and understand complex surgical procedures. The overarching goal is to
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these challenges by: Developing predictive workload, lead-time estimation, material planning models to capture the high variability in HMLV environments using hybrid AI (combining machine learning, feature-based
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quantification in science, engineering, and machine learning. Your workplace You will be employed at the Division of Applied Mathematics in a welcoming and international work environment. The research group in
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decentralized machine learning. Welcome to read more about us at: https://liu.se/en/organisation/liu/isy/ks . For more information about working at ISY, please visit: https://liu.se/en/article/open-positions
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The AITHYRA-CeMM Joint International PhD Call in Molecular Medicine and Artificial Intelligence (m/f
for both life scientists and computational scientists/machine learning experts will start in September 2026. We offer: a highly interactive introductory program in a supportive peer-group environment
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context
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processing (computer vision & machine learning) Where to apply Website https://sede.uvigo.gal/public/catalog-detail/28364578 Requirements Research FieldEngineering » OtherEducation LevelMaster Degree
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as well as profound knowledge of professional computer-aided design and 3D modelling In addition, you have experience in CAD/CAM (preferably McNeel Rhinoceros) and/or robotic fabrication, as
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, release kinetics under biologically relevant triggers. The successful candidate will work at the interface of organic synthesis, chemical biology, and machine learning to guide linker design and optimise