106 machine-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" Fellowship positions in United Kingdom
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Are you completing / have completed a PhD in electrical machines or electromagnetics and looking for an ambitious next step in your research career? We are looking for a talented and highly
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Organisation: Cranfield University Faculty or Department: Faculty of Engineering and Applied Sciences Based at: Cranfield Campus, Cranfield, Bedfordshire Hours of work: 37 hours per week, normally worked Monday to Friday. Flexible working will be considered. Contract type: Fixed term...
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Organisation: Cranfield University Faculty or Department: Faculty of Engineering and Applied Sciences Based at: Cranfield Campus, Cranfield, Bedfordshire Hours of work: 37 hours per week, normally worked Monday to Friday. Flexible working will be considered. Contract type: Fixed term...
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the fields of Statecraft and Military Sciences, contribute to SST:CCW’s research outputs, support visiting fellows, and engage with academic and practitioner communities worldwide. The successful candidate
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About the Role A fantastic opportunity has arisen for a Senior Research Fellow to join the Power Electronics, Machines and Control (PEMC) Research Institute at the University of Nottingham and
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An opportunity has arisen for a Research Fellow in Machine Cognition in UCL Linguistics, within the Division of Psychology and Language Sciences and Faculty of Brain Sciences. UCL is ranked eighth
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discrete choice modelling, behavioural data science or machine learning? Are you interested in developing the next generation of AI tools that accelerate scientific discovery while maintaining behavioural
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tailored to the needs of the aerospace industry (e.g., for use in engine control systems). The main purpose of the role is to conduct (or lead) research in machine-checked cryptography as part of
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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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, fabrication materials and orbital environments. Using a small number of representative test cases, we will trial Machine Learning-based predictive methods to estimate material condition and indicative reuse