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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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. Specifically, we develop small molecule photo(cyclo)addition reactions that allow for the efficient formation of covalently bound reaction products under (visible) light irradiation. Importantly, the thus formed
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materials is still evolving, especially for temperature- and rate-sensitive systems like prepreg. This project aims to address these challenges by developing a suite of deployable tools and predictive models
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wrinkles can occur, which cause parts to be scrapped. To minimise material and energy wastage, digital models of the manufacturing processes can be developed and linked to process control and optimisation
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have implications for food security and conversation. The successful student will explore innovative synthetic biology approaches to develop rapid, low-cost, and field-deployable tests for detecting
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on knowledge development and the application of principles set out in lectures and course materials. Training and support are provided to help them develop teaching related skills and enhance professional
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Modeling for Hydrogen Storage (m/w/d, E13 TV-L, 75%) Responsibilities: Develop a pore-scale model for two-phase flow of hydrogen gas and brine Develop a bioreactive-transport model and couple it with the two
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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PhD Studentship: Robust quantum control for quantum error correction The development of fault-tolerant quantum computing is one of the most coveted aims of quantum technology. It will bring about a
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methods, which can struggle to represent underlying soil processes. Your job This project aims to develop a new generation of soil mapping approaches by combining mechanistic understanding of soil formation