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Excellent skills in statistical modelling, preferably using R Proven track record of publishing in international peer-reviewed journals as first author Willingness to conduct fieldwork and participate in
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Your Job: We are looking for a researcher to develop and apply machine learning models for genomic data in our lab. We focus on sequence analysis, genomics, semantics, and cross-domain data
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plan that focuses on the development and combination of 3D-generative models and potency predictions for drug design. A successful research proposal to our question will focus on these topics: How
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(including the doctoral dissertation) Strong methodological training in quantitative survey and experimental research (Additional asset: experience with using large language models in surveys) Proficiency in
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of in vitro and in vivo pre-clinical models, including hiPSC-derived systems The postdoctoral project will combine experimental (wet-lab) and computational (dry lab) approaches Be part of Geman Center
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
), statistical analysis, modelling, and mapping Highly motivated and eager to work in an interdisciplinary marine research context Excellent communication and teamwork skills, with the ability to collaborate
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Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean"...
statistics and the ability to apply quantitative analysis to ecological data A strong background in programming (preferably in R), including data manipulation, statistical analysis, and spatial modelling and
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Postdoc position: mechanisms of autoimmunity & autoinflammation in inborn errors of immunity (m/f/d)
erythematosus (SLE). This project will integrate biochemical, immunological, and imaging techniques, along with co-culture of patient-derived organoids and model organisms combined with various –omics approaches
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network
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topic from the areas represented within the group. The group’s interdisciplinary focus includes not only classical topics in numerical analysis, such as the analysis of nonlinear PDEs, but also modeling