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quantitative analysis skills and experience developing algorithms and/or conducting statistical analyses with biological datasets. Background and work knowledge in statistics, algorithms, optimization of novel
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Fully funded Ph.D. opportunity in Aerospace AI. Sponsored by EPSRC and BAE Systems covering tuition, fees and a bursary of up to £19,569 (tax free) + £7,500 industrial top-up. Combinatory Artificial
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sensing, and Electromyography (EMG) tools to understand user-device interaction and optimize real-world rehabilitation performance. The student will gain experience in AI, human biomechanics, smart textiles
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resource-constrained environments, and it is important to investigate whether features derived from different network layers can be effectively combined. Machine Learning Model Development & Optimization
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(i.e. red agents). However, due to a fragmented market, rapid technical developments, and nascent research the extent of capabilities and optimal solution architectures are not well understood. Current
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process. Address blind inverse problems by defining a network to learn distortion functions from data, informing the optimization in the learning process. Refine optimization and learning strategies
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the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
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designing and developing experimental equipment suitable for containing the liquids at the temperatures needed, as well as optimizing the quality of the data obtained, both through experiment design and
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optimal operating conditions and followed by surface analysis techniques (e.g. Scanning electron microscope, X-ray diffraction for residual stress measurements, Electron Back-Scattered Diffraction and
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in a more accurate analysis of optimizing the service performance. Computer vision approaches such as ones for object identification and action recognition can help to automatically identify deviations