503 computational-physics-"https:"-"https:"-"https:"-"Univ" PhD positions in United Kingdom
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candidate will: Hold an MSc (or equivalent) in Physics, Mathematics, Computer Science, or related fields Have strong computational and modelling skills, including proficiency in Python and experience with
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Start date: 28/09/2026 Funding Sponsored by Cranfield University, this full-time PhD Studentship will cover the PhD programme fees and provide an annual bursary of £20,780 (tax free) for three years
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Generative AI applications continue to expand, optimizing computational efficiency is becoming increasingly critical, particularly for AI in resource-constrained environments or at the edge. To address
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that you apply early as the advert may be removed before the deadline. This PhD project aims to develop a virtual tabletting laboratory by creating computational models that capture the multiscale mechanics
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inclusive society programme. The project aims to address key challenges on enhancing climate resilience and sustainable development in rural and small urban communities by implementing transformative nature
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for applicants with a degree in Computer Science, Mathematics, Physics, or Engineering. Prior experience in AI is necessary. Prior experience in tomographic imaging and medical physics would be advantageous but
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using 3D Systems Phantom Touch devices (robotic arms which provide force feedback of simulated physical contact while measuring movement kinematics and force output). These will be integrated with bespoke
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scintillators to X-rays and gamma rays using radioisotopes and X-ray generators. The student will be registered on the Physics PhD program; however, the nature of this research project is highly multi
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under relevant process conditions. Rational mutagenesis and computational protein design will then be applied to enhance lithium specificity and operational robustness in complex, high-impurity leachates
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engines will no longer rely solely on centralised on-board computers; but will leverage distributed, multi-layered control architectures and off-board computational power to optimise performance in real