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. The research group is seeking a talented Doctoral Researcher in nonlinear systems and control with strong interest in nonlinear stability theory, modeling & identification, optimal control, certifiably safe
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position will contribute to the research programme of the recently founded "AI Hub in Generative Models", a research consortium funded by EPSRC. The goal of the programme is to do research in the area of
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of research proposals, co-supervision of researchers and PhD students, organization of events, and coordination of research spaces. Main Duties and Responsibilities Perform the following activities in
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equations into AI-based models to solve fluid sensing problems in a robust and efficient manner. Your role may include developing new optimization techniques, coding new algorithms, creating new mathematical
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coding ability. Research Associate: Hold a PhD in Engineering, Mathematics or a closely related discipline, or equivalent research, industrial or commercial experience. *Candidates who have not yet been
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integration of genomics, proteomics, epigenomics and transcriptomics from these models alongside extensive human tumour collections. You will be PhD-qualified, or have submitted your PhD thesis for examination
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mathematical training is essential, particularly the ability to understand and verify mathematical proofs. A clear motivation to pursue a PhD or an academic career in finance or economics is also required. Our
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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, epigenomics and transcriptomics from these models alongside extensive human tumour collections. You will be PhD-qualified, or have submitted your PhD thesis for examination at the point of commencing this post
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, economic viability, and robustness to realistic operational uncertainty. PhD (or equivalent) in control engineering or closely related discipline. Track record in at least two areas: model predictive control