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Funding A highly competitive EPSRC Quantum Technologies Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years. Training and support will also be provided. Lead Supervisor’s full name & email...
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A highly competitive EPSRC Quantum Technologies Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years. Training and support will also be provided. Lead Supervisor’s full name & email address Professor...
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Funding A highly competitive EPSRC Quantum Technologies Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years. Training and support will also be provided. Lead Supervisor’s full name & email address Dr....
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Funding A highly competitive EPSRC Quantum Technologies Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years. Training and support will also be provided. Lead Supervisor’s full name & email address Dr...
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an appropriate discipline. Subject Area: Computer Science & IT
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the Schools of Chemistry and Chemical Engineering at the University of Leeds, will investigate flow chemistry and machine learning approaches for the development of an automated reaction screening platform
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characterisation (FIB-SIMS, XPS) at the Bragg Centre for Materials Research at Leeds University. The project will be carried out at Leeds University, with several secondments to the AWE site for X-ray tomography
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Engineering at the University of Leeds (UoL) and by Diamond Light Source (DLS). The successful student will spend 50% of their time at the UoL and the other 50% across crystallography beamlines I11 (High
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project presents an exciting opportunity for an ambitious scientist or engineer to work across the boundaries of chemistry, physics and engineering, with opportunities to develop a broad portfolio of skills
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@leeds.ac.uk Project summary Ligand/material discovery has been carried out through laborious trial-and-error approaches. Recent advances in high throughput computational chemistry and AI/Machine Learning