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candidates with: • Relevant subject matter experience at required level (e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science) • Willingness to adapt and work across different
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will need to contact the project supervisor to discuss. Online applications are made by clicking the 'Apply' button, above. Please select PhD in Mechanical Engineering on the Programme Choice page. You
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Funding: School of Computer Science Studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email
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Research Assistant/Associate in Photonics Integration of Graphene and Related Materials (Fixed Term)
on integrated GRMs. The successful candidate will hold a PhD (or be close to obtaining) in Applied Physics, Material Science, Nanotechnology, Chemistry or Electrical Engineering with experience in photonics
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PhD Studentship: Dissecting the Molecular Architecture and Function of LMTK3 as a Therapeutic Target
of interest, skills and previous experience, motivation for Doctoral Research, future goals, and why you are applying to this project. On the application system select Programme of Study – PhD
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This fully funded PhD studentship, sponsored by the EPSRC Doctoral Landscape Awards (DLA) and RES Group, offers a bursary of £25,000 per annum, covering full tuition fees. The project focuses
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Improving recovery from pneumonia after hospitalisation Project description: Applications are invited from UK students for a three-year PhD studentship funded by the NIHR Nottingham Biomedical
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Overview: This exciting PhD opportunity is at the intersection of aerospace engineering and cutting-edge technology. It focuses on developing an innovative ground-based robotic inspection system
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science