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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics
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, to support predictive modelling, deep phenotyping, and real-world evidence generation. Apply and refine causal inference methodologies, such as structural equation modelling and Bayesian approaches, to better
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acquired at multiple resolutions (tissue-level, single-cell/nucleus, and spatial transcriptomic data), requiring complex integrative analyses. The successful candidate will lead the analysis of a large
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, to provide in-depth technical support in data science, artificial intelligence (AI) and research software engineering. Team members are assigned to multiple long- and short-term research projects from across
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computing (HPC) environments and include data assimilation techniques in a Bayesian framework. Under the guidance of a mentor, the participant will identify and integrate multiple data streams into the model
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, multiple sclerosis, epilepsy, and other neuroinflammatory and aging-associated conditions. The successful candidate will contribute to the design and optimization of serologic assays/platforms and the
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/Seurat, count models, batch correction, differential analyses). Strong grounding in statistics (GLMs, hierarchical/Bayesian modeling, multiple testing) and experimental-design principles. Bioinformatics
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, as part of the development, one position located in Görlitz as Research Associate / PhD student (f/m/x) Integration of CMOS detector technology into the Universal Bayesian Imaging Kit (UBIK) (subject
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and carbon cycle model-data integration using the CARDAMOM Carbon-Water Bayesian model-data integration framework. The candidate will help advance global land biosphere estimates of biomass, water
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strategies to mitigate impacts on adjacent waters. Research activities include: coordinating with multiple stakeholders and collaborators to define objectives and research questions; leading participatory co