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Field
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abort (or not engage) if the bright white lines that fit a defined and rigid expectation are not clearly visible. These systems use algorithms, rather than AI machine learning, to detect road markings and
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Primary supervisor - Prof Kate Kemsley Join us to research and develop advanced analytical methods for tackling food fraud head-on! Economically motivated adulteration of foods is a significant
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, thermal, electromagnetic or kinetic), are critical for the sustainable operation of wireless IoT devices and remote sensors. The world can reduce reliance on batteries and fossil-fuel-derived power if more
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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4-Year PhD Studentship: Deciphering how domain organisation regulates heparan sulphate function Supervisors: Prof Cathy Merry, Prof. Kenton Arkill, Dr Andrew Hook Overview Glycosaminoglycans (GAGs
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and social care research (Prof. Anderson), applied statistics and analysis of large routine datasets (Prof. Abel), and substantive knowledge of the abuse of older people and related policy issues and
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Early Years (www.isey.org ) in London, UK. Supervision will be by Prof Sam Wass (www.profsamwass.com ) (UEL, UK), with Prof Davidson (UEL, UK), Prof Rachel Barr (Georgetown, US) and Dr Sarah Jessen
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be by Prof Sam Wass (www.profsamwass.com ) (UEL, UK), with Prof Davidson (UEL, UK), Prof Rachel Barr (Georgetown, US) and Dr Sarah Jessen (Lübeck, Germany) as co-supervisors. The PhDs will start in
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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Supervisory Team: Prof Middleton, Prof Gandhi PhD Supervisor: Matt Middleton Project description: We know of only 20 or so black holes in our galaxy yet predict there should be 10s of millions