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testing. We aim to develop next generation electrochemical energy technologies through holistic views of ammonia/methanol fuel cells and metal and metal ion (Li, Na, Ca etc.) batteries, from the nanoscale
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Abuse (DVA) experiences of people with Multiple Sclerosis (MS) with a view to highlighting the implications for clinical practice. A recently completed PhD (PhD1) study, supervised by Dr Andrea Hollomotz
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of mechanical and robotic systems •Ability to use finite element modelling and to simulate complex mechatronics •Ability to implement control and kinematics with hardware-in-the–loop •Background with relevant
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approaches, including principal component analysis and machine learning, to handle multivariate datasets. Prior experience in data science is not essential; the successful candidate will be supported through
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treatments. To date, there are few techniques that integrate AI and digital twins to improve patient outcomes. Your Role In this project, you will develop new methods that combine AI and digital twins
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insulation systems becomes critical to maintaining performance, reducing downtime, and extending asset life. Generator stator windings typically comprise multiple sub-level insulation systems, including Main
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treatments. To date, there are few techniques that integrate AI and digital twins to improve patient outcomes. Your Role In this project, you will develop new methods that combine AI and digital twins
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elements offers moderate strength and relatively high productivity compared to its highly alloyed counterparts. However, automotive aluminium alloys are susceptible to natural ageing at room temperature
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product sizes and enabling direct on-site manufacturing. The successful candidate will explore innovative methods to realize large-scale on-site 3DP by addressing two key technical challenges: 1) How
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key area of patient safety that can be improved with the use of computer vision approaches to system analysis. For many clinical procedures there can be multiple deviations in service delivery, which