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considered. Application procedure and conditions We kindly request applicants to provide their nationality for statistical purposes only, as part of our commitment to promoting diversity and ensuring equal
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of Tübingen. The project is led by Principal Investigators Dr. Marlen Fröhlich, Prof. Dr. Michael Franke (both Tübingen) and Prof. Dr. Manuel Bohn (Lüneburg). The successful candidate will support the project
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knowledge in plant species identification. The successful candidate should be able to independently conduct statistical analyses in R and hold a valid driver's license. Experience in GIS and handling large
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analyse large datasets such as the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics to identify activity related to the treatment of community acquired pneumonia. This will require
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Assoc Prof Liton Kamruzzaman, Prof Hai Vu, Prof Graham Currie, Prof Eric Miller (University of Toronto), and Prof Roger Vickerman (University of Kent). Together, the team aims to: Define sustainable size
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skills (e.g., programming, statistics) or a willingness to develop them will be considered particularly important for this project. Admission Regulations for Doctoral Studies at Stockholm University. About
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post; public engagement or outreach • Preferably demonstrable experience in academic writing for publication, e.g. peer reviewed paper(s) and/or report(s) • Advanced statistical software skills
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, quantification, and data analysis, including statistics. Variations of liquid extraction-based techniques, based on nano-DESI and electrospray ionization, will be developed further and coupled to modern mass
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spectrometry for molecular characterization of biological samples, using tandem mass spectrometry and reactive chemistry, quantification, and data analysis, including statistics. Variations of liquid extraction
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | about 2 months ago
related to staff position within a Research Infrastructure? No Offer Description The Quantitative Genetics research group is interested in developing statistical genomics toolboxes to decipher the genetic