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Field
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are linked to research on composite hydrogen tanks, composite propellers for drones and finite element modelling of textile manufacturing. All research will be conducted with leading companies in
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skills include: Interest or background in composite materials, particularly in modelling and/or testing Basic understanding of finite element methods (FEM); any exposure to impact or burst mechanics is a
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. Aim You will have the opportunity to build a high-fidelity process simulation and perform experimental validation to assess the structural performance of composite sleeves under operational conditions
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well as their cleanroom fabrication by silicon micromachining will be investigated. The main challenges are (1) the design and modelling of new sensor topologies, (2) development of the MEMS fabrication processes, (3
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dioxide (SO2) are commonly measured. Each pollutant is produced and destroyed by different processes, and the levels of the various pollutants are correlated with each other, for example, and increase in
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the potential to accelerate materials design and optimization. By leveraging large datasets and complex algorithms, ML models can uncover intricate relationships between composition, processing parameters, and
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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of methane dynamics in rapidly changing ecosystems and contribute to improving predictive models of future methane emissions. Field sampling will focus on regions where methane cycling is still poorly
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will lie on developing machine learning models for regression and reinforcement tasks to work with, enhance or replace established methods from computational engineering and computer simulation (such as
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the formation rates or composition of a biominerals from known environmental conditions. This project aims to construct such a model. The ultimate goal is to create a general framework for predicting