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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 19 days ago
vision models to monitor and track 3D objects from videos. Duration: The research fellowship will have a duration of 12 months. It’s expected to begin in January 2026 and may eventually be renewed. It is
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About the Project Project details: The rapid evolution of battery technologies drives global lithium demand, and the UK’s 2030 vision seeks a competitive, sustainable battery supply chain to fuel
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2025 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within
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Peter Hamilton, and Professor Ray Yep), and who also share a vision for public engagement of the discipline. The studentship is funded by an endowed fund at the University. The Centre encourages and
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limited to Professor Robert Bickers, Dr. Vivian Kong, Dr Peter Hamilton, and Professor Ray Yep), and who also share a vision for public engagement of the discipline. The studentship is co-funded by a
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to master both. You will be assessed against the responsibilities and qualifications stated above and the following general criteria: Research experience Experience and quality of teaching Research vision and
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Large vision–language models (LVLMs) can describe driving scenes and support decisions [Li25], but they sometimes hallucinate objects, relations, or events that are not present [Liu24,Liu25]. In a
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within the Institute of Health Informatics. The studentships will commence from Feb 2026. About the project Successful applicants will work with the programme coordinator to identify a project and a
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learning and experience in two or more of: computer vision, sensors/sensor fusion, robotics fundamentals. • Proficient in programming languages such as Python and C++; experience with frameworks such as
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the field of multi-modal data analysis and generation, integrating computer vision, natural language processing (NLP), and machine learning to enable innovative interactions with unmanned aerial vehicles