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Field
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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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electromagnetic and thermal simulations. This work will include detailed tissue segmentation from medical images, integration of automatic and semi-automatic segmentation algorithms, refinement of anatomical
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of two years. About the Project The successful candidate will advance the algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems
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. Subject description Automatic Control comprises the analysis and synthesis of models and model-based algorithms for control, estimation, and monitoring of complex dynamic systems. Project description
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theory, such as method of moment (MoM). • Experienced in computational algorithm on Finite Element Method (FEM) • Possess knowledge of electromagnetic theory, such as Dyadic Green’s function and
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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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Assistant (RA) or a Postdoctoral Research Associate (PDRA). The appointed candidates will support advanced research initiatives focusing on systems design, distributed systems, and algorithmic optimization
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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