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awareness These funded PhD scholarships are suitable for students with a background in Computer Science, Mathematics, Engineering and Cognitive Science. Students with interests in machine learning, deep
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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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, addressing engineering questions in how future electricity networks can remain stable and resilient as renewable generation grows. Grid-forming (GFM) control is increasingly recognised as a critical enabling
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investment yet in the vibrant and strategically important field of Metamaterials research. Explore a career and grow your experience of research and professional life in and around the Physical Sciences with
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operational efficiency and sustainability in remanufacturing contexts This funded PhD studentship is open to highly motivated candidates with a strong background in Engineering or a closely related discipline
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in the natural and human world can shift suddenly and irreversibly. Understanding such critical transitions ranks among the most urgent challenges in modern ecosystem sciences and societal preparedness
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of light. [1][2] This emerging technology holds enormous potential, offering routes towards new forms of highly parallelised information processing (optical computing), point-of-care diagnostics, next
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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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, structure, defect chemistry, and ionic transport behaviour. Comprehensive materials characterisation will be undertaken using structural, thermal, and electrochemical techniques, including X-ray diffraction
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-time, or pro rata for part-time study. The student would be based in the Faculty of Environment, Science and Economy at the Streatham Campus in Exeter.