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this challenge, the Faculty of Engineering's Department of Built Environment and Doctoral School of Engineering and Science offers a PhD stipend. The stipend is open for appointment within the doctoral programme
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if the process overall runs most efficiently with high tar load before the POX or if a low tar load is preferred. Modelling (chemical engineering models, computational fluid dynamics and/or models with detailed
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, microfabricated devices, 2D materials, catalysis and/or surface science. For Topic 4, candidates must have documented skills within computational modelling of atomistic processes. For all topics, experience in
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and transport systems, within the ELLIS network. The project is fully funded through the Novo Nordisk Foundation Data Science Distinguished Investigator programme. You will be based in the Intelligent
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interdisciplinary collaboration. This PhD project is focused on numerical simulation of resistance spot welding with existing electro-thermo-mechanical finite element modeling as a foundation. Computational fluid
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background in chemical engineering thermodynamics, physical chemistry, water, electrolyte systems, or a related field experience in mathematical modelling, scientific programming excellent written and spoken
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should develop an independent research proposal that relates to and addresses these issues. Your research will be based on qualitative social science methods, e.g. case study research, participant
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have an interest in quality news and AI-based chatbots. We expect: A Master’s degree in either computer engineering or computer science/data science. Experience with AI, Large Language Models, RAG, data
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Technology, Computer Science, Artificial Intelligence, Data Science, Robotics or any other relevant field, at the time of admission into this PhD program. Students currently enrolled in Master’s programs can apply, provided
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science with outstanding results. The candidate should have thoroughly documented laboratory experience from either an organic chemistry setting, or a molecular biology setting. Statistical training and