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for and writing of your PhD thesis. The other 25% are for training and academic service to the Faculty of Arts, including teaching. Where to apply Website https://www.academictransfer.com/en/jobs/362721/phd
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challenges. Designing the next generation of aircraft, spacecraft, and propulsion systems requires numerical simulations that are both more accurate and computationally more demanding than ever before. While
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PhD Candidate: Modulating plant-based nutrition to enhance the sensory aspects and nutritional value
acceptance; Write a project proposal and obtain medical ethical approval for a randomized controlled trial; plan, coordinate and perform a human dietary intervention study; Write high-quality papers
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development. What you do You will be primarily responsible for experimental design and development of AI-driven computational tools for detection and non-invasive prediction of DNA replication defects and
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strong interest in the design and operation of laboratory-scale bioreactors. An independent, self-motivated, and curiosity-driven mindset. Strong analytical skills combined with a scientific mindset and a
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urban hydrometeorology, climate modelling, heat-health interactions, and water-sensitive urban design? Do you want to work on a high-impact, international project that translates urban hydrometeorological
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climate and hydrometeorological modeling, urban biometeorology, and climate-sensitive urban design? Do you want to pursue a PhD study that translates advanced urban (bio)climate and hydrometeorology
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PhD Position Simulating the Airspace of the Future: Fleet & Network Optimization for Future Aircraft
funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Design the simulaton models that will help airlines fly future aircraft types profitably
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and process design. This project is embedded in the HyCarb consortium , where academic and industrial partners collaborate on advancing sustainable carbon conversion technologies under realistic
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-wide traffic prediction Design physically consistent and interpretable machine-learning methods for dynamic traffic systems Test and validate prediction models using large-scale real-world traffic data