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Daniel Marsh – [email protected] Project summary The School of Chemistry is offering a PhD studentship, funded by the Science and Technology Facilities Council (STFC), in the general areas of planetary
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technologies. There is a global effort, both through industrial R&D and fundamental research, to develop ammonia fuel technology, especially for maritime transport of goods and power generation. A major
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concentration of CO2 represents a huge challenge, particularly in terms of scale, given that a large power station can produce over 50,000 tonnes of CO2 per day. Current technology utilises amines such as
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chemistry, organic synthesis, and programming/reactor automation, which you will use to create new approaches for reaction screening and optimisation. In addition, this work will be conducted in collaboration
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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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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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an appropriate discipline. Subject Area: Computer Science & IT
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the construction industry and those commissioning, designing and delivering projects. The transition to sustainable construction embraces multiple actors and stakeholders, including government, technology providers
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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