770 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions in United Kingdom
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funders. It is essential that you have a PhD in computer science, or equivalent professional qualifications and experience; ideally your PhD or equivalent professional qualifications and experience will be
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and access appropriate training and professional growth. About the role We are delighted to invite applications for a PhD training fellowships at the Cicely Saunders Institute, King’s College London as
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the University’s international reputation as a hub for cutting-edge research. Candidates must have a PhD degree in Power Electronics, Machines, and Drives or a closely related field, with a proven track record in
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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the application form, which is limited in the number of characters it will allow.) Interviews: 4th and 5th of August 2025. About you The successful candidate must hold a PhD in mathematics or computational biology
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medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts
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are included but clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data