47 high-performance-computing-postdoc Fellowship positions at University of Nottingham in United Kingdom
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programme. To acquire, analyse, interpret and evaluate research findings/data using approaches, techniques, models and methods selected or developed for the purpose. To establish a national reputation and
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individual and collaborative research in the area of Power Electronic, Machine and Control. The role holder will be expected to conduct and lead high-caliber, impactful research at the forefront of Power
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-holomorphic Hilbert Modular Forms”. The central aim of the project is to develop explicit algorithms for computing with non-holomorphic Hilbert Modular Forms and using these algorithms together with theoretical
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-based role focusses on electromagnetic design, computational modelling (e.g., COMSOL, CST, ANSYS), dielectric characterisation, and testing that help to bridge the gap between laboratory-scale research
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based on existing research on high-frequency methods for built-up structures and their vibrational behaviour. For this, we are looking for a candidate who is strong in both the analytical background
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of the literature relating to ‘Blairism’ and to the study of legacy effects in policy analyses A high level of competency with time series modelling OR age-period- cohort modelling OR structural equation modelling A
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. The Research Associate/Fellow will be a human computer interaction (HCI) specialist in qualitative (interview) data collection, analysis, and sensemaking. They should also have a broad understanding of
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deliver research from fundamental to high TRL. The successful candidate will contribute to several projects by managing a growing research team while conducting their own investigations. As a senior
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(http://www.nottingham.ac.uk/utc), in the Faculty of Engineering. The centre boasts an excellent international reputation for high-quality research funded by EPSRC, IUK, and the EU. What we offer: A world
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entitled “White Matter Computation: Utilising axonal delays to sculpt network attractors”. The central aim of the project is to determine how dynamic patterns of neural activity evolve in a complex network