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Systems Research Group (MAS) requires an Experimental Research Fellow who is experienced in the field of multiphase computational fluid mechanics and aero-engine oil systems to work across several industry
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The Mechanical and Aerospace Systems Research Group (MAS) requires an Experimental Research Fellow who is experienced in the field of multiphase computational fluid mechanics and aero-engine oil
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computers”(under the UKRI Guarantee scheme). Topics include: - Quantum many-body dynamics - Quantum algorithms - Quantum-enhanced numerical methods - Quantum machine learning - Tensor Networks - Topological
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in their management and optimisation. This position offers an excellent opportunity to further develop bioinformatics expertise in a dynamic, multidisciplinary environment, with scope to design and
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supervision is essential. The successful candidate will be dynamic, well organised and will have a calm and positive attitude in working collaboratively with a wide range of stakeholders internally and
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machine learning, with particular interest in credit risk models, dynamic survival models and reliable machine learning. The research team has published extensively on topics in machine learning
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skilled postdoctoral researcher to join a small dynamic research team led by Dr Madhu Dandapani focused on paediatric tumour metabolism within the Children's Brain Tumour Research Centre within the
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Global University, and offers unique teaching and research opportunities in a highly dynamic economy. The successful candidate will be expected to make a significant leadership impact within their academic
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will join a dynamic, interdisciplinary team exploring the frontiers of mass spectrometry imaging and molecular modelling. As our Research Fellow, you will lead the computational arm of a Leverhulme Trust
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Medical Sciences Unit of the University of Nottingham. It consists of a dynamic, friendly and multidisciplinary team of researchers from clinical and non-clinical backgrounds with expertise across