64 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Dr" "FEUP" PhD positions at University of Nottingham
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process: http://nottingham.ac.uk/ComputerScience/Studywithus/Postgraduateresearch/NottinghamDTCinAI.aspx Entry Requirements: Applicants are normally expected to hold a 2:1 Bachelor’s degree or Master’s
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requires weekly travel to attend in-person training at these universities, which will be covered by the CDT. For further information about the CDT programme, please visit the CDT website at www.fusion
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datasets, and large-scale statistical studies comparing different methods. The successful candidate will be jointly supervised by: Dr Edward Gillman (https://www.nottingham.ac.uk/physics/people
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funding to enrol on this PhD. For students from China, you are encouraged to apply in partnership with the China Scholarship Council - more information can be found here: https://www.nottingham.ac.uk
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degree in engineering, maths or a relevant discipline, preferably at Masters level (in exceptional circumstances a 2:1 degree can be considered). To apply visit: http://www.nottingham.ac.uk/pgstudy/apply
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with a 1st class degree in engineering, maths or a relevant discipline, preferably at Masters level (in exceptional circumstances a 2:1 degree can be considered). To apply visit: http
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Positions are filled on a first‑come, first‑served basis, so early expressions of interest are encouraged. Supervisors: Prof. Gordon Airey, Dr Anand Sreeram, Dr Nick Thom, Dr Richard Taylor
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, preferably at Masters level (in exceptional circumstances a 2:1 degree can be considered). To apply visit: http://www.nottingham.ac.uk/pgstudy/apply/apply-online.aspx For any enquiries about the project please
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. Gordon Airey, Dr Anand Sreeram, Dr Nick Thom, Dr Richard Taylor Programme length: Four years (full‑time) Start date: 2026/27 academic year Keywords: biogenic supply chains, sustainable materials, biobased
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data-driven methods to develop an inverse design framework for manufacturing systems. Together, we will advance the capability to design manufacturing systems that embed reliability, resilience