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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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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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importance, for triggering shallow landslides in sensitive clays. The focus will be on developing computational models that will quantify the mechanisms, precursors and the time to failure. This will be
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of quick clays. A novel combination of miniaturised thermal-hydro-mechanical experiments and particle level modelling will be pursued to unravel the unique mechanisms that make quick clays so hazardous and
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materials for synthesizing different types of hydrogen storage molecules. Using advanced quantum mechanical calculations, you will develop multi-scale models to study reaction kinetics and improve catalyst
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) methods to tackle challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division, our group advances generative models, molecular simulations
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challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division, our group advances generative models, molecular simulations, and molecular design
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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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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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for Sustainable Housing and buildings”, you will: Develop an ontology of regenerative building production by analyzing how regeneration affects on-site praxis, economic structures, and business models and