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Technology Laboratory (QTL) division of the Microtechnology and Nanoscience (MC2) department, working in a large team of PhDs, postdocs and researchers. About the research We are seeking PhD students to work
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. This position will focus on one key stream: understanding and improving the interaction between EVs and roadside barriers. This will involve: Modelling EV crash dynamics with conventional and SRB barriers
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of the immune system in retinal degenerative disease. We utilize induced pluripotent stem cells, human donor tissue and mouse models of retinal degeneration to study the role of monocyte derived macrophages in
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, surgery planning with patient data for surgeons, real-time remote guidance for maintenance in industrial plants, and iterative design simulation for architecture and engineering. However, its wide adoption
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contact Holger Voos at: Your profile Solid experience / knowledge in the following areas: Design and modeling of soft materials and soft robotic components Modeling and simulation of drones Mechatronics and
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for the new green steels compositions, including impurities and tramp elements. These models should enable density-functional-theory (DFT) accurate large scale atomistic simulations of defects including
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on www.blockades.eu At NTNU’s Department of Modern History and Society, the project team will be located within a rewarding professional environment with an active community of PhD candidates and postdocs. Duties
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interdisciplinary project. The project concerns algorithm design, implementations of algorithms, and simulated and biological data analysis. The student is expected to learn a bit of relevant molecular biology to
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-cycle fatigue. The research methods are based on both small-scale and full-scale experimental testing and on Finite Element Modelling. Are you motivated to take a step towards a doctorate and open
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chemistry and life cycle thinking in early decision-making. AI-based methods and hybrid models, which integrate data-driven and mechanistic modelling, hold significant potential to enhance prospective