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aims to develop a novel theoretical framework for nonlinear and robust control of dynamical systems from a phase perspective. You will have the opportunity to freely explore multiple research directions
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This PhD studentship is a part of Rolls-Royce sponsored research centre developing advanced control systems to enhance the performance and efficiency of new aerospace propulsion systems. We apply a
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platform. Initially, a black box deep learning approach will be implemented. However, due to the need for robustness, transparency, and explainability (e.g. for quality control across sectors), the research
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Project descriptionAutonomous systems are intelligent agents—such as robots, vehicles, or drones—that can sense their environment, make decisions, and act independently. When multiple such agents
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extended from cloud solutions (such as OpenLLMetry), the research question is how to identify anomalies in collected information that can come from multiple AI services either invoked manually by users or by
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and interpretation of results Supporting the analysis of multiple mitochondrial ‘omics datasets, such genomics, transcriptomics and clinical data Collaborating regularly with the team members and
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-B4 Investigators: Prof. Dr. Meng Wang, Chair of Traffic Process Automation , and co-supervised by another expert in traffic control Requirements: excellent or very good university degree
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project will develop novel methods for modelling and controlling large gossamer satellites (LGSs), so that they can be reliably utilised in space-based solar power (SBSP) applications. The candidate will
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phenomena across scales, combining multiple fields including physics, mathematics, astronomy, history & philosophy of science, and social science. Its approach to societal engagement throughout the project’s
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providing quality control or process optimization for electron microscopy in an industrial environment. Experience bringing electron microscopy to industrial application. Experience leading projects