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Field
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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Engineering, Materials Science, or a closely related discipline. *Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant. Expertise in materials selection
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. The candidate should have a good 2.1 Bachelors, or Masters degree in Engineering, Computational or Physical Sciences, or Physiology/Medical Sciences. This project will suit those with a keen interest in
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techniques, and who is keen to contribute to translational science with real clinical potential. Essential criteria Applicants should have (or be expected to obtain) a bachelor’s degree with 2:1 honours. A 2:2
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innovative and dynamic sector, estimated at more than £100 billion and growing at over 6% every year. This project aims to deliver a step-change in hair care technology by developing new strategies to form
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in partnership with SMS Group, a world-leading supplier of metallurgical plant and rolling mill technology. Project: Closed-Loop Electromagnetic Microstructure Control During Hot Strip Mill Run-Out
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guidance, research training, publication support, hardware/compute access and Intel engagement opportunities. Entry requirements: Relevant undergraduate or master’s degree in computer science, AI
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in computer science, AI, engineering, mathematics, physics or related field; strong programming skills; interest in ML, computer vision, robotics, embodied AI or autonomous systems; motivation
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£100 billion and growing at over 6% every year. This project aims to deliver a step-change in hair care technology by developing new strategies to form covalent bonds within hair fibres, moving beyond
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University Technology Centre (UTC) in Electrical Systems at the University of Manchester. The UTC researches a wide range of underpinning electrical technologies for applications in future gas-turbine engines