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based primarily on the relevance and quality of the submitted research plan and secondarily on other scientific merits. Importance will be placed on experience of engagement with non-academic stakeholders
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methods such as in-situ infiltration sampling, topographic and bathymetric surveying. Proficiency in programming languages such as Python and R. Responsibilities Research- Under general supervision, is
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' datasets - genomics transcriptomics, and proteomics Proficiency in R, Python, and other programming languages Expertise in Linux, Git, Docker, and other high-performance computing environments Excellent
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Extensive knowledge of X-ray methods Knowledge of synchrotron science Knowledge of catalysis Experience in energy storage Experience with programming languages (ideally Python) Fluent in written and spoken
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' datasets - genomics transcriptomics, and proteomics Proficiency in R, Python, and other programming languages Expertise in Linux, Git, Docker, and other high-performance computing environments Excellent
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 30 days ago
grant from the German Space Agency (DLR) and by an ERC Advanced Grant (eCMZs) for the study of interstellar medium and star formation properties and physics in the centers of nearby galaxies. The analysis
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of findings (written and oral communication). This position requires extensive prior experience in the field of human integrative physiology, the capacity to work independently, and strong oral and written
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science, computational biology or other relevant fields. Applicants must have excellent skills in English (oral and written) and be independent, creative, and dependable. Applicants should have several publications in
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. You will join the Junior Innovator program at Aarhus University, comprising young researchers from across all faculties. You will be encouraged to and supported in applying for external funding e.g
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by a generous gift from Joan and Irwin Jacobs, will join the cadre of trainees at the Center including those supported by the NIH T32 AIPrN training program. The program is designed to train the next