440 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" Fellowship positions in United Kingdom
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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This post will advance the application of Machine Learning (ML) in weather forecasting and hydrological prediction. The Research Fellow will develop ML methods for postprocessing numerical ensemble weather
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the design, development, deployment and evaluation of NeoShield’s applied machine-learning systems, the machine-learning-driven Clinical Decision Support Algorithm for neonatal sepsis and the real-time ward
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second in the UK for research power and first in England. About the role The project will be carried out at the Department of Computer Science, in the Machine Intelligence Lab (https
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to £43,805 per annum Apply by: 26/04/2026 Role Description We welcome applications from skilled, delivery-focused engineers to contribute to our successful railways-applied smart machines research programme
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-focused engineers to contribute to our successful railways-applied smart machines research programme. About the Role We are seeking a highly motivated and multiskilled Research Fellow with expertise in
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to £43,805 per annum We welcome applications from skilled, delivery-focused engineers to contribute to our successful railways-applied smart machines research programme. About the Role We are seeking a highly
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Institute. The post is available immediately and funded by ARUK until 30 April 2029 in the first instance. For a full job description please visit UCL’s online recruitment portal (https://www.ucl.ac.uk/work
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reconfigurable RF hardware for CAP-MIMO systems and contributing to machine learning-enhanced ISAC methods development through EM-informed modelling and hardware design. This is a unique opportunity to build
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, Veterinary and Life Sciences Graduate School. Full details of the programme are available at http://www.fom.gla.ac.uk/RDTC/. Regular attendance and contributions to research meetings of the research group is