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(UA). This fully funded PhD position offers a unique opportunity to contribute to the future of pandemic resilience through scenario analysis, clinical data collection strategies and implementation
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for many high-tech companies. Job description This role involves the development of cutting-edge chemical sensors based on metal-organic frameworks (MOFs) that leverage kinetic selectivity to differentiate
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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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reflectance infrared Fourier transform spectroscopy (DRIFTS) to unravel charge transfer mechanisms, active sites and intermediate molecules in the reaction pathway. Additionally, finite element theoretical
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pathway. Additionally, finite element theoretical modelling and density functional theory calculations will be used to further increase our understanding of the photo-reduction mechanism. Correlating
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pathway. Additionally, finite element theoretical modelling and density functional theory calculations will be used to further increase our understanding of the photo-reduction mechanism. Correlating
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Directed Energy Deposition (DED) process for metallic components. The PhD candidate will focus on edge computing and the application of AI for data analysis and for identifying correlations with ground truth
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component behaviour. 3. Extend and apply existing core loss models to novel materials, leveraging insights from the MAGNIFY network. 4. Conduct a stochastic study on how strand positioning within
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, that combines diffusion and transformer models, there are clear indications that the analysis of this data can be automated. This will open new avenues in data interpretation and building predictive models
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, with exciting collaborative opportunities globally, as well as within Luxembourg. The doctoral candidate will perform wet lab experiments as well as computational analysis to uncover biophysical