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Field
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guaranteed? This project will focus on developing theory and algorithms for MPC applied to the smart grid. The emphasis is on developing implementable (low-complexity) controllers with strong theoretical
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frameworks to ensure the developed processes are compliant, scalable, and environmentally responsible. Multiobjective optimization algorithms will be employed to balance key performance indicators such as
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algorithms and models, and scientific computing programming (e.g. in MATLAB), and (5) modelling of material degradation and wear-out, reliability prediction models. Familiarity with failure modes of electronic
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development with Python Experience of machine learning with PyTorch Good knowledge of machine learning and computer vision algorithms Ability to work on own initiative and in a team Experience in collaborative
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on transplant using multimodal medical data. You will be responsible for literature review, data cleaning, model development and implementation. You should possess a relevant PhD (or near completion) in
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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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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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, the project will develop machine learning based solutions for predictive grid analytics (such as grid congestion forecast, asset monitoring, etc.). Based on these results, the project will develop
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their career development as a Knowledge Transfer Partnership (KTP) Associate. The candidate will lead development of environmental monitoring technologies, data management and analysis methods to investigate
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reducing odours from pomace and digestate. The project comprises seven work packages. As a leading partner, the University of Surrey will develop a system digital twin (SDT) to enhance overall sustainability