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of the art in mathematical and numerical modelling of CO2 storage? This might be the right position for you! About the project/work tasks: About the project TIME4CO2 TIME4CO2 aims at advancing simulation
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material design process. Some potential key research objectives: AI Model Development: Create machine learning models to predict FGM properties based on compositional gradients and processing conditions
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numerical modelling. The PhD candidate’s tasks will primarily be experimental, to conduct hydrate sealing experiments on pore scale and core scale using MRI, but also to provide input for modelling and
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experiments on pore scale and core scale using MRI, but also to provide input for modelling and numerical simulations. The PhD candidate will collaborate with industry partners and take active part in a larger
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requirement Experience with data analysis and machine learning models is an advantage Experience from molecular modelling or molecular dynamics simulations is an advantage Applicants must be able to work
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is an advantage Experience from molecular modelling or molecular dynamics simulations is an advantage Applicants must be able to work independently and in a structured manner and demonstrate good
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areas within Energy, Climate and Environment and Regional Growth, and integrates the researchers from the Electrical Power Systems (EPS) research group (RG) and Applied Modeling and Control (AMOC) RG
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updates, and interoperability at scale. In close collaboration with project partners, the PhD candidate will focus on relevant data modeling and processing approaches for data gap filling, redundancy
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of microfabrication fundamentals. Experience with CMOS chip design, or proficiency in relevant EDA tools such as Cadence Virtuoso, Tanner EDA, L-Edit. Experience in Multiphysics modelling and simulation with COMSOL
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processes as droplets/condensates wet membrane compartments in cells. Numerical simulations and theoretical membrane models will be developed, aiming to couple viscous interfacial fluid flow, elastic