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on practical feedback linearization with limited or imperfect models. Learning-enabled control dynamics Embedding optimization and learning algorithms (e.g., SGD, Bayesian updates) into control design and
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and tires. The use of such fibers requires new compounding strategies to optimize compound performance. In the proposed study, both dipped and undipped short-cut fibers will be incorporated into tire
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position as Senior Lecturer. Optimization, machine learning, and control theory together form a central toolbox for understanding, analyzing, and controlling complex systems. These fields span deep
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years. Your main duties and areas of responsibility This PhD project addresses data analytics and optimization for routing and control of wind-assisted ships and is part of the WINTEGRATE (WIND+INTEGRATE
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in underground facilities. The project aims to evaluate sensor technologies, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted
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focused on translational control in cancer • Drive the development and advance our single-cell ribosome profiling strategies in mouse models of cancer • Contribute to the development and optimization of new
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optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter
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& Operations leads the day-to-day evaluation and ongoing optimization of financial-related internal controls across a complex academic medical center. This position ensures the integrity of financial operations
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their wings for optimal performance. This PhD project will involve the design, fabrication, experimental validation, aeroelasticity modelling, and control of a small-scale wing prototype with a bio-inspired
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collection and analysis systems. Deploy prototypes in the field and optimize their performance based on feedback. Guarantee the reliability, security and scalability of the IoT solutions developed. Work