530 cloud-computing-"https:"-"https:"-"https:"-"https:"-"https:"-"BioData"-"BioData" PhD positions in United Kingdom
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engineering, or atmospheric science* Expertise in and passion for computational modelling and software development/engineering Expertise in cloud physics or contrails preferred but not required Creative problem
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Scottish Power as part of your program. The project Power Electronic Devices (PEDs) have been proposed as a new approach to increase the uptake of low-carbon technologies (such as electric vehicles and heat
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the opportunity to undertake a placement within Scottish Power as part of your program. The project Power Electronic Devices (PEDs) have been proposed as a new approach to increase the uptake of low-carbon
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integrating machine learning, computational modelling, and experimental validation. The successful candidate will receive training in both computational and experimental biology within a highly collaborative
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell
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Project Overview This is an opportunity to conduct fully funded interdisciplinary research under the ‘Sustainable Transitions – Leverhulme Doctoral Training Programme’ at the University of Essex
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- or part-time research programme. This includes current doctoral researchers in the College of Arts and Law. The funding will be available from September 2026. One scholarship is available offering
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, delivering greater performance, functionality, and reliability. This demands the adoption of faster switching wide bandgap devices and greater system integration. About This PhD This PhD programme is part of a
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) at the Rutherford Appleton Laboratory (RAL) in Didcot. This is an exceptional opportunity to work at the interface of artificial intelligence, computational imaging, and life sciences, supported by the Ada Lovelace
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Project Overview This is an opportunity to conduct fully funded interdisciplinary research under the ‘Sustainable Transitions – Leverhulme Doctoral Training Programme’ at the University of Essex