44 parallel-and-distributed-computing PhD positions at University of Nottingham in United Kingdom
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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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challenges to the electricity transmission and distribution system, as solar power is not dispatchable and therefore its incorporation as a major element of the generation mix requires the accurate prediction
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vesicles (EVs). The PhD studentship will run in parallel to this work, conducting cell engineering and data synthesis to address the hypothesis that specific snoRNAs are causative in disrupting HCM metabolic
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Electrical power level studies with developed digital twin to identify visible solutions for distribution electric propulsion. We are seeking exceptionally motivated and first-class students with a strong
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PhD Studentship: A continual learning approach for the development of robust robotic control systems
. AI is expected to be at the centre of these systems, being the foundation of computer vision, monitoring, and control solutions. Despite the promising results that AI (and especially Deep Learning) has
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business model for commercialising the enzymatic conversion process. The model will focus on local distribution and benefits for the local communities in South Africa. Applications are invited from students
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EPSRC Doctoral Landscape Award: Embedded power electronics converters design for future electric vehicles Applicants are invited for a 4-year EPSRC Doctoral Landscape Award PhD programme, co
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a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction. The algorithms developed in
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University Technology Centre (UTC) in Manufacturing and On-Wing Technology, The University of Nottingham. Applicants are invited to undertake a 3-year PhD program in partnership with the UK Atomic Energy
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key area of patient safety that can be improved with the use of computer vision approaches to system analysis. For many clinical procedures there can be multiple deviations in service delivery, which