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high-quality research on interpretable and learning-based stochastic optimal control for over-actuated electric vehicles, with a focus on ensuring robustness and fail-safe operation. You will: - Develop
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the Swedish Center for Sustainable Hydropower (SVC). SVC promotes collaborative research across universities and industry to support the future of hydropower. Simulations will be performed using resources from
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position for candidates interested in interpretable AI, stochastic optimal control, deep learning and high-impact research in sustainable mobility. About us The position is located at the Systems and Control
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the preservation of cultural and historical values, and contribute to accessible, high-quality housing. The project will apply qualitative and participatory methods such as focus groups, interviews and workshops. A
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strategies. Explore the technical, on-site, and managerial innovations and tools needed for contextualizing this ontology in Sweden. Perform action research and case studies in construction sites
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partners over the past five years. Major responsibilities Your major responsibility as a PhD student is to perform your own research as part of the research group in CAM2 Centre at Chalmers. The position
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The position generally also includes teaching on Chalmers' undergraduate level or performing other duties corresponding to 20 percent of working hours Contract terms and what we offer: The PhD positions
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safety. You will work on developing control algorithms all the way to performance assessment in test vehicles. The project combines theoretical aspects of control algorithms, experimental design, and
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Experience with performing laboratory experiments Ability to work with large data sets (> 500 GB) Numerical modelling Main responsibilities Independent research and research training (80% of time) Support for
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development. We offer high-quality education at the bachelor's, master's, and doctoral levels, delivering over 120 courses annually. The department maintains extensive collaborations with academia, industry