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PhD Studentship: Artificial Intelligence for Building Performance – Optimising Low-Pressure Airtightness Testing Supervisors: Dr Christopher Wood (Faculty of Engineering) and Dr Grazziela Figueredo
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engineering. Expertise in numerical tools (Ansys, JMAG, .etc) and programming are desirable. Experience in electrical machine prototype development would be advantageous. Eligibility and Application
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PhD Studentship: Revolutionising Litz Wire Development for Next Generation Ultra-High Speed Propulsion Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers
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computer science or mechanical engineering. The candidate will have programming experience, particularly on the development of machine learning pipelines. The University actively supports equality, diversity and
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the research environment for PGRs. PGRs benefit from training through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed
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through the Researcher Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing
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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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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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participate in the dissemination of findings through publications and conferences. Qualifications: Completed or nearing completion of a Master's degree in Medicinal Chemistry, Chemical Engineering, or a related
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supervisors spans five departments at University of Nottingham including Architecture and Built Environment, Electrical and Electronic Engineering, Mathematics, Physics and Social Sciences. The PhD programme