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systems Strong analytical skills and interest in interdisciplinary, real-world problem solving Good communication skills in English and the ability to collaborate with diverse stakeholders You must have a
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such as CAPeX. You are an experimentalist - second to none. You have experience in one or more of the following areas: Analytical electrochemistry Vacuum science/surface science MEMS chip design and
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into the enzyme-substrate interaction in the same set of enzymes. Further, there will be collaboration with a PhD student from NTNU working with similar analytical methods on another class of carbohydrate-active
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of silicone polymers, including crosslinking and tailoring of soft materials. Foam preparation and characterization, with experience in analysing porosity, microstructure, and density. Advanced analytical
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applications Strong analytical and problem-solving skills Good communication skills and ability to collaborate across disciplines Motivation to make a real-world impact in sustainable energy and industrial
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motivated candidate with a background in chemical or process engineering and strong experimental and analytical skills. Join us to drive innovation in carbon capture and contribute to shaping Europe’s low
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train connections (30 min. from CPH central station). Description of the PhD project The title of the PhD project is PhD Fellow in Biochemistry. This project focuses on developing and applying analytical
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of the research group. Desired qualifications and skills: A relevant background in aquatic biology, animal physiology or a related field. Good skills for laboratory-based analytical tools. Practical experience
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PhD scholarship in Runtime Multimodal Multiplayer Virtual Learning Environment (VLE) - DTU Construct
analytical and technical problem-solving skills, with a solid foundation in computational engineering Ability to work independently and take initiative in planning and executing computational and experimental
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success