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mathematical and analytical models to predict coil loss, facilitating the optimal design of HPMCs Constructing a large-signal platform to measure coil loss of HPMCs Exploring innovative solutions, such as new
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within our portfolio of hypothesis-generating screening studies, a methodological portfolio where we identify signals of interest from real-world register data to guide the conduct of tailored studies. We
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parameters as well as downstream requirements. Maintain detailed records of experimental data, process conditions, and system modifications to support scale-up and system integration. Publish scientific
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project. Your profile We are looking for a highly motivated candidate with a background in machine/deep learning, and communication networks. The required qualifications include: PhD in computer engineering
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landmark database on university deep tech startups, carry out independent, high-quality research leveraging this data, and engage with stakeholders in the Danish and international deep tech arena
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involve the following tasks: Supervision of BSc/MSc and PhD students related to the project. Performing catalytic tests on upgrading pyrolysis oil and pyrolysis oil model compounds using an advanced
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qualifications PhD in molecular biology, biomedicine, biochemistry, or a related field. Experience in genomics tool. Strong analytical, organizational, and record-keeping skills. Good communication skill in
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encompasses a range of research areas in software development, data analytics, and intelligent systems, and it plays a key role in several ongoing European projects. Our workplace is truly international and
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phenological data using camera and drone-based imagery. Use genetic analyses to assess the relationship between genetic differentiation and phenological variation. Develop and implement advanced statistical
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performance of remanufacturing processes by providing data-driven material assessments and validation techniques. Responsibilities and qualifications You will join the Section of Materials and Surface