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Location: South Kensington About the role: The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple
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The role will develop new AI methods for identifying the instantaneous state of a fluid flow from partial sensor information. The research will couple techniques from optimization and control theory
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protocols for electrical biasing of samples in the microscope. A key task is to process and analyse large 4D-STEM data sets and extract information about domain wall structure and dynamics. The role involves
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. The Faculty of Population Health Sciences has established itself as UCL’s largest research-based faculty, encompassing eight institutes. More information available at www.ucl.ac.uk/population-health-sciences
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of environmental economics and innovation. The successful candidate will play an integral role in supporting data-driven, policy-relevant research examining the intersection of environmental challenges and
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. At NTNU, 9,000 employees and 43,000 students work to create knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position
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, and flow cytometry. You’ll work closely with other members of the lab to analyse data, interpret results, and contribute to ongoing publications. You will also be responsible for maintaining accurate
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Current modelling and simulations require either generic assumptions to be made for fluid dynamic based modelling leading to inaccuracies between modelled and experimental data or, intense
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and operations; integrated air and missile defence; information advantage and future aerospace combat power; artificial intelligence) may be taken into account when evaluating an application
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emissions, and enhance occupant health and wellbeing. As a Research Assistant, you will work closely with UK- and Egypt-based teams to analyse collected data, develop and test computer-based retrofit models