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This research opportunity aims to hire PhD students as a self-funded program. By analysing retinal images with deep learning technology, early detection of Alzheimer’s disease may be possible. By
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Industry 5.0 goals. Expect impactful insights into advanced technology integration and human expertise, driving sustainability through improved system performance. Students benefit from collaboration
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Applicants should have a first or second class UK honours degree or equivalent in a related discipline. This project would suit a student with an aerospace or mechanical engineering background. Experience
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are to: Build on the strong synergy between manufacturing, materials, design and management supporting the transition from product service systems (PSS) to through life engineering services as a contributor to
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motivating alignment towards sustainability goals. Cranfield University provides the ideal environment for this research endeavour, offering a wealth of resources and expertise in engineering and digital
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postgraduate university that is a global leader for transformational research and education in technology and management. Research Excellence Framework 2021 (REF) has recognised 88% of Cranfield’s research as
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systems enables effective digitalisation and automation transition. This project aims to build a generative AI model and algorithm for optimising manufacturing systems’ engineering design and processes
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deadline22 May 2024 Award type(s)PhD Start date30 Sep 2024 Duration of award4 years EligibilityUK, Rest of world Reference numberSATM475 Entry requirements Applicants must have a B.Sc. in engineering or a
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background in environmental and civil engineering, water and wastewater, molecular biology, microbiology, biochemistry, or other similar sciences. If you are eligible to apply for the scholarship (i.e. a
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upgrade water treatment infrastructure and increase its resilience using nanobubble technology as a chemical-free approach. The successful candidate will work closely with the water industry to develop