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context. The work will include, but is not limited to: investigating new mathematical formulations of the underlying physics; developing fast algorithms and numerical methods that leverage modern parallel
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backgrounds such as in computer science, mathematics (pure or applied), or engineering. The successful applicant will be highly motivated, have excellent time management, and a proven track record in
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applicant must have (or be close to obtaining) a relevant PhD in Fluid Mechanics from an Engineering, Mathematics or Physics Department, a strong background in theoretical and computational fluid mechanics
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Area Chemistry Location UK Other Closing Date Thursday 31 December, 2025 Location: Diamond Light Source, UK / University of Nottingham, UK Contact: Dr David Duncan Starting date: July 2026 (earlier start dates also possible) Email: david.duncan@nottingham.ac.uk About the project We are...
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need to contact the project supervisor to discuss. Online applications are made via the 'Apply' button above. Please select Engineering Mathematics* on the Programme Choice page. You will be prompted
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performance and explore the use of an ultrasonic sensor for real-time monitoring. Experiment with ultrasonic sensors for real-time seal gap measurement. Combine experimental research and mathematical modelling
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mathematics are welcome to apply. Hands-on experience in optical laboratories will be a plus. How to apply: Please visit the UCL i4health CDT webpage linked above for instructions on the application process. In
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challenges in engineering Desirable: Experience with mathematical modelling, optimisation techniques, or supply chain analysis Background knowledge in bio-based materials, biorefineries, or circular economy
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, engineers, and the general public Qualifications Research Assistant Degree in engineering or numerate subject (e.g., mathematics, physical sciences, computer science) PhD close to completion in field of Power
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had