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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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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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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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participation in national and international conferences is integral to the job. Furthermore, participation in supervising BEng, BSc, and MSc students is expected. As a formal qualification, you must hold a PhD
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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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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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applicants regardless of their personal background. Qualifications and the selection process Applicants for this position must hold a PhD degree (or equivalent level of education) in bioinformatics, data
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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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applicants regardless of their personal background. Qualifications and the selection process Applicants for this position must hold a PhD degree (or equivalent level of education) in bioinformatics, data
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer