187 data "https:" "https:" "https:" "https:" "U.S" positions at Nature Careers in Germany
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biomechanical data during mammalian gastrulation. This position is part of an ambitious interdisciplinary project in collaboration with Professor Shankar Srinivas (University of Oxford) and will be based in Dr
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collect and analyse health data, identify risks, advise government and experts, and develop new scientific methods. We are based in Berlin, Wildau and Wernigerode. Get started now Apply directly through the
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., blood, tissue, stool, etc.), their processing (e.g., PBMC isolation), and biobanking in adherence to ethical guidelines, data protection, and biosafety regulations and maintaining databases
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(e.g. RNAi, CRISPR/Cas9, small-molecules). In this context, we also develop new computational tools for automated analysis and data visualization. These include algorithms and software applications
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and analyzing information relevant to research strategy and assisting in the development of institute-related concepts Your Profile A university degree with above-average grades, preferably a Ph.D. in
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at the Institute of Medical Informatics within the research group “Medical Data Integration Center (MeDIC)” led by Dr. Michael Storck and is embedded in the DFG-funded Collaborative Research Centre 1748, Principles
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information is available from the faculty’s women’s representative (sandra.hemmers@cup.uni-muenchen.de). LMU Munich intends to enhance the diversity of its faculty members. Furthermore, disabled candidates with
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the transfer of new ideas from the lab to real-life applications, improving lives. The Computational Health Center (CHC) at Helmholtz Munich drives research at the intersection of artificial intelligence, data
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multi-omics data resources to drive innovative research REQUIREMENTS: Master’s degree (or higher) in (bio-)statistics, epidemiology, mathematics, or a related discipline Proven proficiency in R Solid
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interdisciplinary and international team of bioinformaticians, molecular biologists, and clinicians to identify (i) targetable lesions for cancer patients in high-dimensional data, including next-generation