192 data "https:" "https:" "https:" "https:" "UNIV" positions at Nature Careers in Germany
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team — comprising biologists, engineers, computer scientists, and medical researchers — develops next-generation computational models to interpret complex biomedical data across multiple scales. Our
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of Federated Information Systems is seeking for the next possible date a Scientific Project Coordinator in part-time (80%) Reference number: 2026-0045 We are seeking a highly motivated scientific project
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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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., 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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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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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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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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(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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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