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propellant space propulsion systems. A significant limiting factor of hybrid propulsion systems is the continuous change in surface area of the propellant grain during the combustion process. This changing O/F
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, and dynamic individual who is a team worker, has a positive outlook, and is adaptable and flexible in their working methods. It is also essential that you are highly experienced in setting up continuous
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development and refinement accordingly. We are looking for a highly organised, driven, and dynamic individual who is a team worker, has a positive outlook, and is adaptable and flexible in their working methods
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model predictive control (MPC) methods to enable large groups of buildings to dynamically form coalitions and provide flexible energy services. Your work will incorporate advanced robust MPC techniques
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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treatment, material and energy flow analysis, integrated data modelling, systems dynamics modelling, circular economy, sustainability assessment performance, decision-support tool design Month when Interviews
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Modern cyber-physical systems (CPS), such as UAVs, next-generation fighter aircraft, and command-and-control (C2) platforms, integrate digital computation with physical processes to make mission
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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. The School of Architecture, Computing and Engineering (ACE) at the University of East London (UEL) is deeply embedded in London’s dynamic and diverse communities. Known for its innovative, impact-driven
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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