354 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" PhD positions in United Kingdom
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will be based at the University of Birmingham and supervised by Professor Russell Beale and Dr Renate Reniers, who bring expertise in the areas of human computer interaction and psychology respectively
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an allied field. An MSc degree in a relevant area is desirable though not necessary. Experience in coding (e.g., Python/R/Matlab) and experience in behavioural experimentation, statistics, or machine learning
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Subject area: Drug Discovery, Laboratory Automation, Machine Learning Overview: This 36-month PhD studentship will contribute to cutting-edge advancements in automated drug discovery through
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projects of the MQS group. Our recent research has focused on the theory and applications of variational quantum algorithms and quantum machine learning. We also have activity in quantum optics, so
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well-structured projects, secondments, and training activities. This comprehensive approach ensures that the candidates acquire the multidisciplinary knowledge and skills required to address real-world
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, robotics, and machine learning. You will work within a multidisciplinary supervisory team spanning engineering, robotics, and computer science, and collaborate with researchers working on real-world
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characterisation and radionuclide partitioning using specialised experimental facilities within the NNUF RADER facility (https://www.nnuf.ac.uk/rader ). This will include the use of state-of-the-art facilities
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structures, access to space, multidisciplinary design and concurrent engineering, uncertainty treatment and optimisation, machine learning. (https://www.strath.ac.uk/ ) Task description for your Individual
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candidates with an exceptional track record may also be considered. For further information and guidance, please contact Dr Mingee Chung at m.chung@bham.ac.uk References: https://www.birmingham.ac.uk/news/2024
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that conduct research with academic leaders across leading UK institutions. Engage in online and face-to-face activities, including cohort-building events and collaborative learning exercises • Funding: A