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the Institute of Health Informatics. The studentships will commence from Oct 2026. About the project Successful applicants will work with the programme coordinator to identify a project and a supervisory team
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, which has an impact on both the individual and health care providers, and can also be a financial burden. However relatively little is known about risk factors for both suffering the injury and recovering
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a role of critical national importance: helping deliver the UK’s nuclear deterrent. We are at the forefront of science, technology and innovation, protecting the UK and NATO allies from the most
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technologies. More information about the group can be found at www.ANMSA.com . We are seeking highly motivated, bright and enthusiastic candidates with a strong background in micro/nanofabrication
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
, Chemistry or Electrical Engineering with experience in photonics, optoelectronics in graphene and related materials, physics of low dimensional systems, plasmonics, near-field optics, CMOS technology and
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density
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@manchester.ac.uk and Dr Amir Keshmiri - a.keshmiri@manchester.ac.uk ) for this project before you apply. Please send your CV along with a cover letter about your motivation to study this PhD project.
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cancer deaths in the UK, and diffuse large B-cell lymphoma (DLBCL) is one of the most common and aggressive subtypes. Standard first-line therapy (R-CHOP) cures many patients but ~40% are refractory
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industry to address challenges in the science and technology of atomically thin semiconductor for low-energy-consumption electronics. Informal enquiries about this studentship can be made to Prof Elena
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell