52 phd-in-mathematical-modelling-population PhD positions at University of Nottingham
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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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Discover your career The world of the University of Nottingham is defined by our people and the values we share. Our environment is an ambitious vision brought to life across vibrant and forward-thinking global campuses. An ever changing world where open minds and diverse cultures are able to...
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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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PhD studentship: Improving reliability of medical processes using system modelling and Artificial Intelligence techniques Supervised by: Rasa Remenyte-Prescott (Faculty of Engineering, Resilience
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PhD Studentship: Carbon Nanotube (CNT) Winding Development for Electric Motors The Manufacturing Technology Centre UK, and University of Nottingham This project offers an exciting opportunity
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propulsion systems. The student will receive full training and support in both modelling and experiments. The skills developed in this PhD are highly transferable and will offer career pathways across sectors
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. The subsequent data will then be used to populate machine learning models to predict which molecules to synthesise next, to maximise the binding affinity of the molecules to a target protein. This research aims
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(including 7 full professors) and approximately 120 PhD students and post-doctoral research fellows. The group has excellent facilities for experimental work including approximately 2500m2 of research space
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systems of differential equations. The resulting models will be analysed with analytical tools from applied mathematics and numerical studies in the Julia programming language. The successful candidate
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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and