25 modeling-and-simulation PhD positions at Delft University of Technology (TU Delft) in Netherlands
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studies, develop sophisticated simulation models, and co-create practical tools with CCs through interactive workshops. Your research will transform theoretical governance principles into actionable
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their surfaces and can be observed. Join a highly interdisciplinary ERC-funded team combining laboratory experiments, modelling and JWST data to explore the most promising extraterrestrial environments for life
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Help push the boundaries of urban wind modelling by developing efficient
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Systems department. You'll collaborate with an interdisciplinary team working at the intersection of simulation, optimization, and policy modelling. You will also connect with TU Delft's strong internal
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Infrastructure? No Offer Description Join our ERC SPHINX Team to model how climate risks ripple through housing markets. Help uncover when homes become stranded assets and fuel systemic impacts. Help shaping
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of asphalt mixtures exposed to climate-induced stresses such as temperature fluctuations, moisture variations, and UV radiation. The study will involve both laboratory experiments and numerical modelling
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simulation scripts. TU Delft will support your research with access to the DelftBlue supercomputer for large-scale and intensive modelling. In the second phase, you will work / help our technicians
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
of simulation, optimization, and policy modelling. You will also connect with TU Delft's strong internal network on climate action, particularly through the Climate Action Programme and its Climate Governance
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elements/agents, adjusting expectations, mechanisms of contagion dynamics, feedback loops, and non-linear tipping of system dynamics. The SPHINX research program aims to fundamentally advance simulation
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where trustworthiness is essential. Today’s explainability methods often do not provide meaningful decision support and, in some cases, can even leak sensitive training data. Models are especially