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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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exploring appropriate origami folding patterns, as well as manufacturing approaches such as selecting curing or ‘dual-resin’ systems to build the fold lines into the structure. The PhD is within a Centre of
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, this is only available to those eligible for UK fees. All applications should be made online via the above ‘Apply’ button. Under Campus, please select ‘Loughborough’ and select Programme ‘Chemistry’ Please
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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Background Access to healthcare is an ever-increasing global issue. Over 15% of the current global population require rehabilitation care, but the majority of them do not receive healthcare access
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Project details: Iron is crucial for athlete health and performance, yet iron deficiency is a common diagnosis in active populations. Iron deficiency can hinder haematological adaptations and reduce
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health, and intelligent operations. It is particularly suited to candidates with academic backgrounds in Industrial Engineering, Aerospace Engineering, Management Science, Operations Research, Transport
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and Health Data Science Mode of study: Full or Part-time study is possible Project Description: The successful candidate will work in a highly interdisciplinary environment as part of a vibrant cohort
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or materials science & engineering. Knowledge and experience of working with polymers and rubbers/elastomers is a distinct advantage. Funding and Eligibility: UK only Key Information Supervisors
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that forty years of conventional hard robot technology and shape-flexible systems will imminently be superseded by a new soft robotics era with soft multifunctional materials in which Electro Active