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reproducible analysis workflows Familiarity with computational models of vision and machine learning methods (for example CNNs, deep generative models, encoding models) is preferred but not required Ability
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: Scientific Area: Data Science and Engineering; Electrical and Computer Engineering; Computer Science and Engineering; Artificial Intelligence; Computer Vision; Computer Science; Data Science; or Mathematical
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will have the opportunity to develop and implement independent research projects during the fellowship period. Qualifications: Doctoral degree in public health, personal health informatics, psychology
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the work in the lab. Experience with nonhuman primates, systems neuroscience, electrophysiology, behavioral analysis, or computational modeling of vision is especially desirable. The successful candidate
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computer vision techniques, transformer architectures, and multi-modal learning. Familiarity with reinforcement learning (RL) principles, curriculum learning strategies, and the challenges of real-time
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of this programme. The profile PhD in computer vision, computational biology, physics or a related discipline Demonstrated expertise in image analysis and working with large-scale imaging datasets Strong expertise in
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foundation models for general manipulation based on vision-language-action models Develop learning from human demonstration (LfD) framework for vision-language-action models Develop multi-embodiment
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for healthy longevity and its vision of enhancing health and extending healthspan through accelerated translation from bench to bedside. By working closely with clinicians in NHG Health and scientists in NTU
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and program-wide seminars and journal clubs. They will have opportunities to drive undergraduate and graduate students and to teach new skills to technicians and other members of the research group
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The University of Surrey is a global community of ideas and people, dedicated to life-changing education and research. We are ambitious and have a bold vision of what we want to achieve - shaping