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an ongoing large-scale survey study Correlating data with existing baseline and follow-up data Contributing to project deliverables, and Contributing to the synthesis of policy recommendations. The applicant
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Job Description Are you interested in contributing to the development of digital twin software for additive manufacturing (AM) of large-scale fibre composite structures? If so, the Department
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planning, conducting, and publishing epidemiological studies using large-scale observational data, primarily register-based, focusing on women’s short- and long-term health outcomes within the specific work
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qualifications: Strong skills in structural modeling (preferably discrete choice models or matching models). Proficiency in Python or R for data processing, modeling, and simulation tasks. Experience with large
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tasks include planning, conducting, and publishing epidemiological studies using large-scale observational data, primarily register-based, focusing on women’s short- and long-term health outcomes within
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experimental techniques and approaches, including but not limited to molecular and histopathological characterization of tissues, organs, and cells, large-scale expression analyses, use and development of ex
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metabolic data and correlate NMR readouts with physiological function. Preferred Qualifications: PhD in Bioengineering, Chemistry, Biophysics, or a related field. Extensive hands-on experience with organ-on-a
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to new ideas, share our values of scientific curiosity and creativity, be open and honest and conduct research responsibly, be excited about large challenges and enjoy solving them, have an
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evaluation using nasal epithelial models and tissue Contribute to in vivo validation in an established mouse model of acute seizures Analyze pharmacokinetics, biodistribution, and therapeutic efficacy data Co
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and interpretation of proteolysis signatures). Apply molecular biology and microbial engineering to investigate functionality of microbial systems, particularly involving large membrane-bound enzymes