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novel opportunity to automate and improve the frailty assessment process, aiming for greater consistency and predictive accuracy. Aims i) Develop a deep learning algorithm to autonomously detect and
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degradation modes. Evaluating suitable sensor technologies and data sources for acquiring relevant metrics. Developing tools and algorithms to automatically analyse sensor data, assess asset condition, and
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will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold
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mapping using a team of highly mobile legged or legged-wheeled robotic platforms. The research will investigate advanced algorithms for multi-robot coordination, dynamic path optimization, and collaborative
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modular, scalable, and transparent control algorithms suitable for real-time implementation across different vehicle platforms. - Contribute to theoretical developments in stochastic model predictive
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autonomous by embedding machine learning algorithms to search through different reaction parameters Person Specification Candidates should have been awarded, or expect to achieve, EITHER: A Bachelors degree in
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photorealistic game worlds. To achieve this goal, we need advances in many areas, from light transport, sampling, geometry and material representations, and computationally efficient algorithms to display
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Background in probabilistic methods Experience with the application of AI algorithms and probabilistic methods Good programming skills Personal characteristics To complete a doctoral degree (PhD), it is
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to): Transparency and contestability in AI Accountability in algorithmic infrastructures Technical mechanisms for evaluating and auditing AI systems Governance of open-source and general-purpose AI models Data rights
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considered. Experience of using machine learning algorithms and toolsets, ideally in a research context. Strong programming skills (e.g., Python, Java, C++). An interest in physiological signals. Home Student