61 evolution "https:" "https:" "https:" "https:" positions at Delft University of Technology (TU Delft)
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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on methods and models that enable developers and civil servants to align AI tools for use in public organizations. More specifically, your research will focus on the technical development of a flexible and
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collaborate with microscopy scientists on data aquisition questions. We’re looking for someone with excellent software development and object-oriented programming skills, ideally with some experience in
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should ideally have: A master's degree in policy, environmental sciences, sustainable development or another field that provides domain knowledge in the field of coupled social-environmental systems
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modelling and planetary science. Each PhD project has a distinct focus but is designed for strong scientific interaction: PhD 1 | Deposition and evolution of ultrathin ice layers You will experimentally
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support for professional development, including training opportunities, access to world-class computational facilities, and a collaborative academic culture where curiosity and scientific excellence
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modelling. Its research themes include the ageing and healing of asphalt materials, the development of low-temperature and recycled mixtures, rejuvenation and circularity concepts, and the multi-scale
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Delft’s training programme. This vacancy is positioned within the globally recognized Department of Hydraulic Engineering (HE), known for its excellence in education, research, and career development
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longitudinal research-through-design methodology. Through iterative tool development, co-design workshops, and empirical studies, you will design and refine value-driven tools that can be applied in day-to-day
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position, you will lead the development of a probabilistic, error-aware surrogate model capable of delivering fast, uncertainty-quantified predictions for complex multiscale–multiphysics processes in OFPV