24 phd-position-in-data-modeling positions at Chalmers University of Technology in Sweden
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urban planning. Research environment This PhD position is part of the Sustainable Urban Water and Environmental Engineering (SUWEE) research area within the Department of Architecture and Civil
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boundaries and towards circularity, resource efficiency and net positivity. In this fully funded PhD student position, which is part of the Formas-financed project “FRESH: Future REgenerative production
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cells. Reference number: 20250273 Application deadline: August 10, 2025 Project overview This 5-year PhD project aims to develop a flexible and general model that enables comparison between different
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of the numerical methods behind CFD and turbulence models. Experience in analyzing CFD data and interpreting simulation results. Excellent command of written and spoken English. Experience writing scientific reports
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qualifications Marine biogeochemical processes Hydrodynamic processes related to ships, turbulence, or mixing Oceanographic modelling Data analysis and programming (e.g., MATLAB, Python, or R) Interdisciplinary
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This PhD position offers a unique opportunity to advance safe and transparent control for autonomous, over-actuated electric vehicles. You will work at the intersection of model predictive control
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own research in a research group. The position may also include teaching on undergraduate and master's levels as well as supervising master's and/or PhD students to a certain extent. Another important
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for a PhD position that combines research in the field of intelligent mission planning and learning-based optimization with real-world applications, in collaboration with Volvo Group. This is an ideal
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awarded no more than three years prior to the application deadline. To be successful in this position, you: have a strong, quantitative background, ideally hands on with modern generative models have proven
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digital twin framework, adaptable to: The level of detail available for ship modelling, The quality of risk-related data, and Quantified model and data uncertainties. The project will advance knowledge