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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
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computational methodologies/AI that advance our ability to design reliable engineered biosystems (if the candidate has strong math/computer science background), or building new robotic measurement and control
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learning/AI, geoarchaeology, environmental science, or computer science would be beneficial, but is not required, depending on equivalent experience. Funding notes: This project is funded by the European
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settings, while providing essential process insights (such as electrode spacing, solvent chemistry, and operational voltage/current conditions) to support further technological development. Project specific
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, please select ‘Loughborough’ and select Programme ‘Mechanical and Manufacturing Engineering’. Please quote the advertised reference number * CSC-26-WS * in your application under the ‘Finance’ section
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honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline. To apply, please contact the main supervisorDr William Fitzgerald - william.fitzgerald
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Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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programming skills in Python/MATLAB, and an interest in digital twin technologies, cybersecurity and machine learning. Entry Requirements Acceptable first degree: Computer Science or related disciplines