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reconfiguration operation. Develop and evaluate fast and efficient scheduling algorithms for fast control and reconfiguration of the optical AI compute clusters. Realize a small-scale compute cluster lab testbed
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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understanding of the interactions between communication, sensing, control, and decision-making in autonomous robotic systems. Experience in developing algorithms for robot navigation, path and trajectory planning
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classification of aluminium scraps and contaminants. Design and validate algorithms for multi-sensor data interpretation and sensor fusion. Conduct laboratory experiments on scrap characterisation and sorting
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. Your task in detail: Design a data pipeline to extract and preprocess activity-relevant features from monitored building power consumption data Develop a classification algorithm (e.g., based on Gaussian
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postgraduate levels. We welcome applications from candidates whose research aligns with any area of Artificial Intelligence, including but not limited to Mathematical Theories for AI: Algorithmic Optimizations
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foundation models. The Successful Candidate Is Required To: • Contribute to research on post-training of LLMs. • Implement machine learning algorithms, experimental frameworks, and research prototypes
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fairness. • Develop and evaluate large language models, foundation models, and agentic AI systems. • Implement machine learning algorithms, experimental frameworks, and research prototypes
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, optimal transport, set evolution and shape optimization, calculus of variations), and/or convex/non-convex optimization methods (first-order algorithms, nonlinear/non-Euclidean gradient descents
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learning algorithms. Collaborating with industry partners to understand operational requirements and developing AI pipelines for analysing visual and sensor data collected during inspection processes