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this industrial PhD studentship in Physics – fully funded by the University of Exeter and Leonardo UK. We’re looking for a student who has a passion for science, with ambition to learn and apply their own ideas
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this industrial PhD studentship in Physics – fully funded by the EPSRC and QinetiQ. We’re looking for a student who has a passion for science, with ambition to learn and apply their own ideas, perspectives, and
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regulatory obligations. Hydraulic simulators are physically detailed but computationally slow and calibration-intensive, limiting large-scale scenario exploration and optimisation. Purely data-driven
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phase of electronic neural networks is highly power-intensive, and their widespread use puts ever-increasing pressure on global energy infrastructure [1]. Photonic circuits guide and process light-based
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machine-learning-based surrogate models to accelerate design and control workflows. This PhD studentship would suit candidates with backgrounds or interests in engineering, physics, applied mathematics
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curiosity. This research project would ideally suit a candidate with a background in one of the following disciplines: Engineering, Physics, Optics, Computer Science, or Natural Sciences, although we are open
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this fully funded PhD studentship in Physics. We’re looking for a student who has a passion for science, with ambition to apply their own ideas, perspectives, and their personal skillset to the discovery and
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signals is hard. Real-world data is noisy, assumptions don't always hold, and the stakes of getting it wrong are high. You'll use and help further develop an open-source Julia-based toolkit, developed by co
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), Engineering and Physical Sciences Research Council (EPSRC) , and the Peter Sowerby Foundation ; in partnership with Health Data Research UK (HDR UK) and the Economic and Social Research Council’s