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Modern autonomous ground vehicles (AGVs/UGVs) in defence operations require sophisticated AI/ML-based control systems for perception, decision-making, and adaptive responses in complex, unstructured
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navigation services provider, has established the Centre for Doctoral Training in Air Traffic Management pioneering new research in machine learning and AI for air traffic control. During their first year
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systems, from sustainable energy grids to autonomous mobility. In this ERC-funded PhD project, you develop cutting-edge game-theoretic control and optimization methods. Job description Modern society
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significant reductions in manufacturing-related CO₂ emissions. The ambition is to develop innovative technological solutions that reduce energy losses and minimise environmental impact throughout the product
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: combining the best of modern learning methods with the best of control systems theory. The ultimate goal is to develop autonomous underwater robots that can learn, adapt, and make intelligent decisions while
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significant reductions in manufacturing-related CO₂ emissions. The ambition is to develop innovative technological solutions that reduce energy losses and minimise environmental impact throughout the product
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selection criteria You must have a Master's degree in cybernetics, control systems, or equivalent, with a strong training in control of marine vehicles and/or robots. Additional training in machine learning
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investigate CO₂ desublimation under controlled cryogenic conditions. The project is primarily experimental, supported by modelling and data analysis. You will design, build and operate a cryogenic experimental
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Are you fascinated by how machine learning can enhance control without compromising safety or stability? As a PhD candidate, you will develop scalable methods for expressive and flexible neural
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with other scientists at TU/e and more recently within the newly formed Casimir Institute to develop the required solution for many of the relevant challenges in communication and sensing systems