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is available from 01 January 2027 or later. You can submit your application via the 'Apply' button above. Title PhD Position in Physical AI: Adaptive Foundation Models for Robotics Research area and
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machine learning research software, preferably using Python and PyTorch. An interest in foundation models, self supervised learning, multimodal learning, and 3D perception. An affinity for translating
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, visiting researchers, master's students, etc.) Research Context Recent advances in mobile robotics have been driven by remarkable progress in perception, deep learning, and control. However, current robotic
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Job description Next generation automotive imaging radars provide increasingly rich 3D information, opening the door to more advanced scene understanding and perception tasks such as 3D object
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such constrained environments. This project seeks to address these limitations by developing a novel robotic perception framework that enables autonomous navigation for retinal microsurgery. The methods developed
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Are you interested in advanced computational modelling and state-of-the-art scientific software? Join us in creating the Bayesian multiverse: a computational framework for robust statistical analyses
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) acquired during natural sound perception and use state-of-the-art AI models to investigate how the human brain transforms acoustic information into meaningful representations. By comparing representations in
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robot learning and edge deployment. Closing date: 15 August Overview Robotics is entering a new phase where foundation models connect perception, language and action. Vision-language-action models, robot
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, particularly EEG; • Programming experience, particularly in R and Python; • Proficiency in statistical methods commonly used in psychology and neuroscience (ANOVA, linear models, etc.); • Familiarity with
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on the development of first-time-right design tools for multi-optics imaging systems, to be implemented in harsh condition vision applications. In modern perception, navigation and quality inspection applications