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The project will require coding for data analysis and statistics, as well as NGS data analysis. Experience with either of these would be advantageous but an eagerness and commitment to learn is more important
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with animal models, transcriptomics (long read, single cell), multi-parameter flow cytometry, molecular biology and fluorescence imaging will be preferred. We offer an interdisciplinary research team
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. Experience in working with animal models, transcriptomics (long read, single cell), multi-parameter flow cytometry, molecular biology and fluorescence imaging will be preferred. We offer an interdisciplinary
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Postdoc Pathways) If you enjoy working in a very collaborative, supportive and international team, you love writing and improving code, you like analytical thinking and constructive problem-solving, and you
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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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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tissue samples, multi-parameter flow cytometry, molecular biology and fluorescence imaging will be preferred. We offer an interdisciplinary research environment that fosters innovation and collaboration
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coupled with DESI Imaging Mass Spectrometry, HPLC-DAD-MS, HPLC-HRMS, GC-MS, automated extraction systems such as Accelerated Solvent Extraction (ASE) and Supercritical Fluid Extraction (SFE), FT-IR
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
nodule collector prototype Patania II in the Clarion Clipperton Fracture Zone (CCZ) in the subtropical Eastern Pacific in 2021. Undisturbed conditions including their spatiotemporal variability (‘baselines
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within this project include: Extending DeepRVAT towards non-coding genetic variation Applying DeepRVAT to population-scale single-cell readouts Integrating population data with experimental perturbation