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We are seeking a highly motivated Research Assistant/Associate to join EPSRC and industry funded Digital Roads (DR) Prosperity Partnership at the University of Cambridge. This programme is a
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DEPARTMENT OF HEALTH TECHNOLOGY AND INFORMATICS Research Assistant Professor in Medical Laboratory Science / Biomedical Science / Medical Imaging and Radiation Science / Medical Physics / Medical
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, Humanities or other relevant subject. You will have some experience and sufficient specialist knowledge in the discipline to work effectively within the research programme. You will also have the ability
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, Humanities or other relevant subject. You will have some experience and sufficient specialist knowledge in the discipline to work effectively within the research programme. You will also have the ability
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, and Internet-of-Things / Industry 4.0 technologies. Knowledge of computer science principles and modern AI approaches in computer vision and/or time series analysis is a plus. The position requires
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independently *Candidates with a PhD in other disciplines may be eligible if they can demonstrate exceptional problem-solving skills and deep expertise in the development of complex computational models
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computational approaches. You will play a hands-on role in generating and analysing high-quality data that fuels discoveries, publications and therapeutic innovation. https://www.rdm.ox.ac.uk/research
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problem-solving skills and deep expertise in the development of complex computational models. Candidates who have not yet acquired their PhD would be appointed at the Research Assistant level. The
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About us UCL Mechanical Engineering is a leading department committed to advancing engineering excellence for a sustainable future. We are home to a vibrant community of academics, researchers, and students driving innovation across marine, energy, robotics, and materials engineering. With...
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or sensor arrays. Experience generating, processing and analysing large material property datasets including correlating between multiple techniques, or developing computational reconstruction techniques