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are poised to re-define our future mobility. However, full autonomy is not possible without all-weather perception for which Radar sensing/imaging is essential. This project focuses on developing algorithms
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, memory, and energy requirements. The successful candidate will explore novel algorithms and model-design strategies that allow AI systems to operate effectively on edge devices, clinical environments
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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predictive performance, computational efficiency, and spatial resolution through algorithm optimisation, tuning, and refined covariates. Assess trade-offs between spatial resolution and other performance
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algorithms that allow robots to refine their control strategies based on observed human behaviour. Collaboration: The project will benefit from extending existing collaborations between the University
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of speeds from walking to maximal sprinting. Derive and compare algorithms to auto-detect key frames of foot contact and toe-off, required to quantify contact, flight and swing times. Compare lower
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/PYTHON/R/C programming • Application of Machine Learning Algorithms Additional Information Benefits This scholarship covers the full cost of tuition fees, an annual stipend at UKRI rate (currently
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to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will develop a novel hierarchical control framework
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using cutting-edge computational techniques, including machine learning algorithms. Work collaboratively with an interdisciplinary and international team to refine and validate regional wave and ocean
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CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low order strategy practical