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Challenge: Unravelling potential capacity in railways Change: Advancing train trajectory coordination from minutes to seconds Impact: More reliable and sustainable rail transport The ever-increasing
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feedback and past experiences. The PhD candidate will develop innovative planning algorithms which exploit elements of robotic learning. The research will especially focus on planning for small drones
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. The PhD position is part of the sub programme “Living Lab Learning Environments”. Aim is to develop a scientifically grounded co-creative approach to innovation which is reproduceable and scalable. It aims
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mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit www.tudelft.nl/phd for more information Read more about working at TU
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requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles
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collaborate with members of the Chair to develop and refine methodologies that help us link historical analysis and long-term thinking on quaywalls and bridges in the context of the World Heritage property
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as well as to present the findings at (inter)national conferences or workshops. Furthermore, the candidate is supposed to carry out minor teaching tasks (at most 15% of the contract time). Teaching in
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because of the extreme conditions. Numerical simulations can prove very valuable to help at the design stage and to develop efficient control strategies; but they need to be of sufficiently high fidelity
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. The PhD research will commence with an inventory of the already existing inspection and maintenance data and will first develop a method to get structured insight into degradation over time based
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topologies (sampling problem) and angular estimation algorithms (estimation problem) is required, while considering the physical effects (EM problem). The novel idea in DoAnt is to develop an EM-driven