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of Power-to-X processes for renewable fuel production. The project is primarily rooted in process engineering while also addressing the interaction between Power-to-X plants and the electrical grid. You will
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dissemination is expected to focus on leading Human-Computer Interaction venues. For further information about the project, see: https://dff.dk/en/our-funded-projects/meet-the-researchers/research-leaders
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In this PhD position, you will help design the next generation of circular plastics systems by combining hands-on polymer processing with data-driven modelling. The PhD study is a full-time, fixed
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, power conversion, energy and hydrogen storage, downstream processes, and energy demand, to enable more efficient, flexible, and economically viable Power-to-X operation. This position is expected to start
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, processes, and practices, and how educational institutions prepare students to navigate these changes and contribute to the development and implementation of AI. The project may draw on perspectives from
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, computer engineering, computer science, data science, mathematical engineering, robotics, or a closely related field. The candidate should have solid mathematical and analytical skills and a strong interest
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deep learning, computer vision, medical image analysis or unsupervised learning is an advantage. English language skills, both written and spoken Qualification requirements PhD stipends are allocated
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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paradigms that support collaborative processes rather than isolated individual use. Combining perspectives from the learning sciences, Computer-Supported Collaborative Learning (CSCL), Computer-Supported
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processes, feasibility testing, and health economic evaluation will be considered an advantage. Qualification requirements PhD stipends are allocated to individuals who hold a Master's degree. PhD stipends