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outside the UK, in a relevant area of Physical Science, Materials Science, or Engineering. You should be able to demonstrate some computational capabilities (e.g. evidencing a 2:1 or higher in relevant
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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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Project Description The next generation of signal processing, computing and artificial intelligence (AI) technologies will be unlocked by the close integration of electronics and photonics
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geolocated social media data, and computational techniques from network science and machine learning. It is interdisciplinary, combining theories of healthy and accessible cities with computational data
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outside the UK, in a relevant area of Physical Science, Materials Science, or Engineering. You should be able to demonstrate some computational capabilities (e.g. evidencing a 2:1 or higher in relevant
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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
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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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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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candidates will have: A solid grounding in mathematical or physical sciences Interest in dynamical systems and complex system behaviour Some coding experience (Python and/or Julia) Enthusiasm for working
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, etc.), or the equivalent qualifications gained outside the UK, in a relevant area of Physical Science, Materials Science, or Engineering. You should be able to demonstrate some computational