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into technological and economic value. The project combines large-scale longitudinal data on PhD recipients with complementary sources such as publication and patent data, and aims to enrich these with other sources
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, and sustainability transitions; Interest in material flow analysis, life cycle assessment, environmental modelling, and scenario analysis; Experience with programming and/or data analysis (e.g. Python
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for a sustainable and healthy future. From forest fires to big data, from obesity to malnutrition, and from molecules to the moon: we cover the full spectrum of the natural sciences. Our teaching and
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, and how strain rate influences material behaviour. The resulting experimental data will be used to validate numerical models and contribute to certification by analysis of future composite structures
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. Demonstrable experience with data processing, signal analysis and programming (e.g. Python/Matlab). Ability to work independently as well as in a multi-disciplinary team. Fluent in English (oral and written
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. If you possess hands-on experience in cell culture, molecular biology, or data analysis, that is fantastic—but please note that we do not expect you to master all these technologies on day one. We
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a circular and low-carbon economy. The PhD is expected to cover: Developing dynamic material flow analysis (dMFA) models to quantify steel stocks, flows, scrap generation, and scrap quality across
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biological environment (including nanoparticle functionalization); spectral data analysis and interpretation; scientific writing. You will work here The research is embedded within the chair of Biophysics and
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doctoral network on quantum error correction, as a PhD candidate at TU/e, designing next-generation codes and decoders for real quantum hardware. Information Quantum computers have grown remarkably fast
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that integrate performance evaluations, processing windows, and circularity-related data. Where to apply Website https://www.academictransfer.com/en/jobs/362528/phd-digital-twin-frameworks-for… Requirements