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technologies including triboelectric nanogenerators (TENG) and other self-powered sensors, flexible and stretchable sensor technologies, electronic skin and soft robotics areas. This will include the design and
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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developing cutting-edge computer vision and deep learning aimed at optimising inspection and monitoring of infrastructure. Applying these advanced technologies to real-world infrastructure challenges through
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Key Information Funding providers: IQE and Faculty of Science and Engineering, Swansea University Subject areas: Materials Science, Electronic Engineering, Physics Project start date: 1 October 2025
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description of: Your research interests Any relevant past research or project experience Supervisor: Dr Shreyank Narayana Gowda Email me at shreyank.narayanagowda@nottingham.ac.uk to express your interest
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Silicon has traditionally dominated as the semiconductor for power electronics, but its inherent limitations such as narrow bandgap (and thus operating voltage) have led to an increasing focus
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Funding Source: Warwick Industrial Fellowships Sponsor Company: Electronic Arts Stipend: £19,237 (increasing annually ) Eligibly: Home students Start Date: July 2025 Project Page – Centre Project
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neurodevelopmental status is recorded in electronic records, if at all, and variations in the outcome measures used. The aim of this PhD, which is part of a major new NIHR project on youth mental health, is to work
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Location: United Kingdom (Remote or Belfast, NI Office based) Position Overview We are seeking a Senior Machine Learning Researcher with deep expertise in computer vision and hands-on experience
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alongside a leading industrial partner (Siemens), which brings together a number of world leaders in power electronics and energy conversion to develop solutions for Industrial motor drives as a primary