14 postdoc-numerical-modeling PhD scholarships at Chalmers University of Technology in Sweden
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and numerical modelling of natural clays at both laboratory and field scale. We are active members of the ALERT Geomaterials network and other international committees. Our diverse and international
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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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with GKN Aerospace Sweden with partial work there and combines numerical and experimental research. Project overview The purpose of this project is to contribute to the development of an ultra-efficient
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numerical modelling of natural clays at both laboratory and field scale. We are active members of the ALERT Geomaterials network and other international committees. Our diverse and international team of over
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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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evaluation frameworks and/or the development of energy system optimization models. The research is applied and closely linked to industrial interests and needs. About the research Our research aims to provide
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division at the department of Electrical engineering at Chalmers. Here, a team of PhD students, post-docs and senior researchers are working on modeling and numerical optimization of problems in the areas
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postdocs at Chalmers, and collaborate with academic and industrial partners in Sweden and internationally. The role also offers opportunities for travel and engagement with external collaborators. Research
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utilized to mitigate flooding risks through hydrological modelling and stakeholder engagement.Focusing on the Gothenburg region, the project will: Identify roads suitable for climate adaptation in three
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introduces new and underexplored vulnerabilities to network-based threats. The goal of this research is to uncover such threats, evaluate their impact on training performance and model integrity, and develop