503 computational-physics-"https:"-"https:"-"https:"-"https:"-"Brookhaven-National-Laboratory" PhD positions in United Kingdom
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complexity and surface roughness, using computational methods like physical optics and the discrete dipole approximation. Supervisors Entry requirements Applications are invited from individuals with a
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also be involved in a fixed number of teaching hours during your time with us. In year one, alongside your specific PhD research modules, you’ll complete a short teacher training programme by
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training programme by the University of Liverpool. As well as gaining a formal HE teaching qualification, you’ll learn key pedagogical skills for the academic job market, primarily through shadowing mentored
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also be involved in a fixed number of teaching hours during your time with us. In year one, alongside your specific PhD research modules, you’ll complete a short teacher training programme by
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or a 2:1 in a relevant field (for example Physics, Electrical and Electronic Engineering, Computer Science, or Biosciences). Funding support After a suitable candidate is found, funding is then sought
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scheme. Access to approximately 40 industrial, government & research partners from the wider aviation sector as part of the Net Zero CDT programme. Access to world class research and education facilities
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: School of Physics & Astronomy Studentship, in collaboration with Merck, providing the award of full academic fees, together with a tax-free maintenance grant at the standard UKRI rate of £21,805 per year
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admissions process generally, please contact research.degree.admissions@plymouth.ac.uk The closing date for applications is 12 noon on 24 April 2026. Shortlisted candidates will be invited for interview
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click on the Apply button above. Please see here for a list of supporting documents to upload with your application. For more information on the admissions process generally, please contact
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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio