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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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We are looking for a PhD candidate fascinated in modelling erosion processes in sensitive clay slopes. The highly sensitive clays, called quick clays, can change from solid to liquid with small
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Join us to pioneer next-generation generative models that accelerate molecular dynamics. We seek a postdoctoral researcher to develop AI surrogates for molecular dynamics (MD), slashing
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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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knowledge base. Main responsibilities include: Conduct benchmarking and further development of risk assessment models and components. Investigate the reliability of accident data, including cross-validation
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work. A model is to be developed to estimate the material mass breakdown for various cell designs and cell formats. The model will be validated from teardown analysis of commercial lithium-ion battery
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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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investigating the fundamental response of quick clays using X-ray based experimental methods and fundamental modelling Experience in programming Merits: Understanding of the mechanics of colloid systems
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for validation of CFD results. Implement novel unsteady CFD and next generation in-house AI based design tools validated by the gathered experimental data on GKN resources with tight collaboration