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Infrastructure & Environment PhD opportunities Our Infrastructure and Environment research division seeks to respond to current societal challenges through the ideas and creativity of engineering
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molecular magnets, plasmonics, battery technology, medical imaging agents and applications for 3D printing. PhD: 3-4 years full-time; 6-8 years part-time; Thesis of Max 80,000 words MSc (Research): 1-2 years
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PhD-level research distinguished by industry interaction. EngD: 4-5 years full-time; 8 years part-time; Apply now Overview Overview The Engineering Doctorate in Sensor and Imaging Systems is four
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Electronics and Nanoscale Engineering PhD opportunities The Electronics and Nanoscale Engineering research division is home to more than 200 research students working in fields as diverse as
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Max Planck Institute for Human Cognitive and Brain Sciences • | Leipzig, Sachsen | Germany | about 2 months ago
London (UCL), UK Teaching language English Languages Courses are held in English (100%). Full-time / part-time full-time Programme duration 6 semesters Beginning Winter semester Application periods
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Watt Kingston Upon Thames Middlesex University Manchester University Reading University Edinburgh University ST Andrews University UCL Durham Fees and funding Fees and funding Fees 2026/27 UK: £5,238
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with a USB connector) Webcam Apply now Overview Overview Our doctorate is comparable to a PhD in terms of scale and rigour. It differs in that it provides a structured programme of advanced study in
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works with companies developing photonics-enabled products and services, from consumer technology and mobile computing devices to healthcare and security. Each of our collaborations is built around an
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Systems, Power & Energy PhD opportunities Systems, Power and Energy research within the School of Engineering is tackling many strategic and challenging areas of research in energy, ultrasonics
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defining environmental policies such as setting biodiversity targets. This project will aim to construct spatial models of biodiversity, explicitly accounting for the temporal structure of the data