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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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activities are focused on the modelling of the mechanics of structures, components, and processes along with involved materials, thereby providing mechanics-based solutions and virtual solution tools to our
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with expertise in materials characterisation, computer vision, computational modelling, and machine learning. The other PhD positions connected to the project are: PhD Student Position in Generative
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machine learning, computer vision, and materials science. The focus of this position is on development of neuro-symbolic models for the effective behaviour of the complex microstructure of recycled
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Materials Science are found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation Product Development Material Production - and in the interaction between these areas
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found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation Product Development Material Production and in the interaction between these areas. The research covers
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This PhD position is part of the WASP-WISE NEST project RAM³ – a multidisciplinary research effort at the intersection of machine learning and materials science. The project brings together PhD
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to construction processes, policies, or material flows. Familiarity with research or practice at the intersection of building production methods, circular business models, and sustainability transitions. Experience
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be paid to the following experiences: -Experience in sampling and analyses of building materials -Experience in Life Cycle Analysis in construction sector. -Experience in building information modeling
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Admission to Doctoral (PhD) Studies in the subject Engineering Sciences with specialization in Biomedical Engineering at the Division of Biomedical Engineering, Department of Materials Science and