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Field
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of
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. Key Accountabilities • Design and develop embedded AI algorithms for appliance profiling using smart meter data • Benchmark performance against state-of-the-art NILM approaches using datasets like
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-based and index-based approaches, the sequent-peak algorithm, extreme value analysis, and multivariate copulas. Based on this, you will develop an improved method to map global energy drought risk and
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for behavioural and security properties; efficient algorithms for model checking, learning and synthesis; improved explainability and safety of machine learning models, e.g. by integrating neural and symbolic
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novel multi-objective optimisation algorithms, to evaluate metrics such as material circularity, system efficiency, cost, and carbon footprint. The University of Surrey is ranked 12th in the UK in
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the theory of optimization algorithms and high-dimensional statistics to address some of the most fundamental questions in ML such as the behavior of neural networks. The environment of this project is highly
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strengthen your application: Experience with computational ship hydrodynamics Experience with STAR-CCM+ and Abaqus Knowledge of Control Algorithms (e.g. PID controller) Knowledge of Reduced Ordered Models What
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CST Microwave Studio, HFSS or EM Pro for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm
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and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms. Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining