42 computational-physics-"https:"-"https:"-"https:"-"https:"-"Ulster-University" PhD positions at University of Nottingham
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in Chemistry, Physics or related subject. The selected PhD candidate will work with Prof Elena Besley on computational modelling of next-generation semiconductors made from atomically thin materials in
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in Chemistry, Physics or related subject. The selected PhD candidate will work with Prof Elena Besley on computational modelling of next-generation semiconductors made from atomically thin materials in
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The School of Computer Science at the University of Nottingham is pleased to invite applications for a fully funded PhD studentship in deployable, efficient, and trustworthy computer vision. This is
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to the interests of one of the School’s research groups: Cyber-physical Health and Assistive Robotics Technologies Computational Optimisation and Learning Lab Computer Vision Lab Cyber Security Functional
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development (such as 3D modelling, VR, animation or interactive design) with inclusive design and accessibility. This project bridges computer science and social care to deliver a digital health training tool
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We are looking for an outstanding PhD student with either strong background in computational modelling or significant experience of laboratory work, who is keen to work at the interface between
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we are looking for The candidate should have a 1st or high 2:1 degree in mechanical/aerospace/manufacturing engineering, computer science, physics, mathematics, or related scientific disciplines
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underlying physics while enabling real‑time or near‑real‑time predictions. You will work with experts in engineering, CFD, data-driven fluid dynamics and computer science. This PhD provides an excellent
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, limited predictability and slow process optimisation. The PhD sits within an interdisciplinary research environment that combines laboratory experimentation with mechanistic and computational modelling
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autonomy: learning-based systems that can operate safely over long horizons, respect physical constraints, and provide predictable closed-loop behaviour. By grounding reinforcement learning in stability