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Applications are invited for two PhD positions in “Probabilistic Artificial Intelligence” at the Department of Statistics, University of Warwick. The successful applicants will be part of the ProbAI
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there are operational or other objective reasons why it is not possible to do so. Full details of the duties and selection criteria for this role can be found in the vacancy advert on the University of Warwick's jobs
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found in the vacancy advert on the University of Warwick's jobs pages. You will be routed to this when you click on the Apply button.
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Number of Opportunities Available: 1 fully funded place Supervisors: Dr. Bora Karasulu (Chemistry), Dr. Albert Bartok-Partay (Physics) Summary: Solar modules incorporating lead-halide perovskites now exceed the efficiency of conventional silicon modules. However, there is much still to be...
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Number of Opportunities Available: 1 fully funded place Supervisors: Dr Katarzyna Macieszczak (Physics) Prof. David Quigley, (Physics and Scientific Computing Research Technology Platform) Summary: Future quantum technologies preparation and manipulation of quantum systems, but current setups...
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Number of Opportunities Available: 1 fully funded place Supervisors: Peng Wang (Physics) Summary: Ptychography is an emerging computational microscopy technique used to acquire images with resolution beyond the limits imposed by the lenses in conventional low temperature (cryo-) electron...
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Number of Opportunities Available: 1 fully funded place Supervisors: Dr. Radu Cimpeanu (Maths), Prof. James Sprittles (Maths), Dr. Tom Sykes (Engineering) Summary: This project is a pioneering study into the microstructural development inside spreading and solidifying droplets (Fig.1e), to solve...
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Number of Opportunities Available: 1 fully funded place Supervisors: Dr. Gabriele C. Sosso, Chemistry Department, Prof. James Sprittles, Maths Department Summary Embark on a fascinating journey into the world of drug delivery and mass transport in complex biological systems. This project will...
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atomic-level understanding on how they behave under stress or strain, and how defects in their crystalline structure affect their performance under different temperature-pressure conditions. This PhD
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model parameters to match available reference data. In this PhD project you will build on the atomic cluster expansion (ACE) approach (e.g. using the ACEpotentials.jl or MACE codes) to tackle inverse