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Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute (HKI) | Jena, Th ringen | Germany | 7 days ago
25 May 2024 Job Information Organisation/Company Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute (HKI) Research Field Biological sciences » Biology
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imaging. Quantitative imaging analyses using image J and Amira. Bioinformatic analyses using R. Mathematical modeling for systems biology. Performs other duties as assigned. Annual Salary Range
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experience in the field of microbial metabolic modeling and evidence of interdisciplinary collaborations are a merit. A strong background in mathematical biology is also a strong merit. Application The
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Leibniz Institute for Natural Product Research and Infection Biology | Jena, Th ringen | Germany | 7 days ago
Job Advertisement Leibniz-HKI-16/2024 The Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) investigates the pathobiology of human-pathogenic fungi and identifies
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, and courage they need to tackle big challenges in the real world and guide diverse communities and organizations. About the College of Natural Sciences and Mathematics The college is home to five
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experience in the field of microbial metabolic modeling and evidence of interdisciplinary collaborations are a merit. A strong background in mathematical biology is also a strong merit. Application The
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Evolution BIOL 306 Neural Basis of Behaviour BIOL 308 Ecological Dynamics BIOL 309 Mathematical Models in Biology BIOL 395 Methods in Organismal Biology BIOL 427 Herpetology BIOL 432 Limnology BIOL 465
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network theoretic models The lab is affiliated with the Department of Ecology and Evolutionary Biology, the Department of Mathematics, the UT One Health Initiative, and NIMBioS and the candidate will be
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24 May 2024 Job Information Organisation/Company Rheinische Friedrich-Wilhelms-Universität Bonn Department Institute of Organismic Biology Research Field Mathematics » Other Physics » Other
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technologies required to make biology predictive; establish mathematical whole cell models for in silico biology as a powerful complement to traditional in vivo and in vitro approaches; tackle fundamental