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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning
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. Strategies will centre on improved formulations of the mixed-integer constraints, as well as the use of machine learning to accelerate conventional solution algorithms (e.g. branch and bound). The second goal
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the Unconventional Communications and Computing Laboratory (UC2), led by Dr Michael T. Barros, which develops modelling and algorithmic methods for networked communication and computation under real-world constraints
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on developing the simulation models, data models and algorithms required to enable connected cross-disciplinary design and optimisation, laying the foundations for more integrated and intelligent engineering
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optimisation and learning-based control algorithms that can make decisions under uncertainty, using realistic network models and large-scale simulations. These methods will be evaluated on representative UK
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on high-fidelity modelling and test data for both metals and thermo-set composite materials. To achieve this we will explore the use of advanced genetic algorithms and/or Artificial Intelligence (AI
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signal processing methods and a modelling environment, aided by unique hardware-in-the loop, to assess the detection and estimation algorithm performance and determine optimal multistatic configurations
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-of-the-art simulation algorithms to circumvent the slow dynamics leading to high-quality modelling of currently inaccessible experimental quantities. About HetSys: Harnessing Data, Modelling and Simulation
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Integration: This WP develops a Runtime Assurance Layer by deploying lightweight anomaly detection algorithms, such as autoencoders, to flag unsafe AI decisions. It also involves the development of an Ethical
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the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballots using the STV method. Our first point of