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programme at the Faculty of Science . The ideal candidate has a background in or experience with one or more of the following topics: SIMD performance engineering. Machine Learning. Communication-efficient
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consist of survey data from social workers and school teachers as well as qualitative interviews with frontline professionals and families. The project should apply a mixed method approach, but emphasis can
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focusing on place and mobility in the 20th century. We welcome applications from candidates with backgrounds in Danish studies, Scandinavian studies, linguistics or ethnology. The successful candidate will
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independently and in close collaboration with others, and be a benefit to our team Excellent language skills in English and preferably also in Danish, due to the nature of data Our group and research - and what
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mind-set with a strong interest in human immunology and infectious diseases Language skills Good interpersonel skills with a collaborative mindset Place of employment The place of employment is at NIVI-R
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submitted electronically by clicking APPLY NOW below. Please include: Motivated letter of application (max. one page) Curriculum vitae including information about your education, experience, language skills
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preferably has strong programming skills and experience with the modeling and simulations of fluid or solid mechanics or ice sheet flow and deformation (for example by use of finite element/volume methods
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of individual atoms. The development of this technology is crucial for the next generation of quantum device fabrication techniques and has great commercial interest and potential for patenting. The skills and
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of samples. To apply and further optimize data analytical workflows. A comprehensive database will be created, documenting the key chemical and physical properties of advanced bio-oils. The qualified applicant
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tools from chemistry and biology, and apply these in studies of therapeutic peptides and proteins. Our aims are to develop modulators for protein-protein interactions (PPIs) and to provide molecular-level