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the Advanced Materials Research Group (AMRG) at the Faculty of Engineering, University of Nottingham, which amongst its wide research portfolio, conducts cutting edge research into the development of next
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Supervisor: Dr Ni Yang Secondary Supervisor: Prof Ian Fisk Subject Area: Food Science and Consumer Behaviour Research Title Development of consumer-centric methods to measure user-experience heat
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PhD Studentship: A continual learning approach for the development of robust robotic control systems
A continual learning approach for the development of robust robotic control systems This project is an exciting opportunity to undertake industrially linked research in partnership with
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Applications in Challenging Environments, https://www.race.ukaea.uk) robotics and remote handling centre. RACE was founded in 2014 as part of the UKAEA’s Fusion Research and Development Programme to design and
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succeed We aim to give you all the support you need to nurture your talent, develop and reach your professional goals and personal career goals. Equal, diverse, inclusive Our university is a supportive
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Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience Engineering Research Group) Aim: Develop a mathematical model for obsolescence modelling for railway signalling and telecoms
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In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and
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the decommissioning of the JET machine. UKAEA’s wider mission is to lead the commercial development of fusion power and related technology and position the UK as a leader in sustainable nuclear energy. UKAEA is keeping
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, combustion, and process optimisation. The project is focussed on the development of novel interface capturing Computational Fluid Dynamics methods for simulating boiling in Nuclear Thermal Hydraulics
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designing and developing experimental equipment suitable for containing the liquids at the temperatures needed, as well as optimizing the quality of the data obtained, both through experiment design and