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, planning, and performing image analysis The project is in collaboration with international partners, active interaction as a team member is expected Support of relevant user beamtime, i.e. collaborators
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analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
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experiments, molecular cloning, cell culture, and standard laboratory methods such as flow cytometry and RT-qPCR. The computational work includes, for example, the analysis of omics data and computational
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fluids, flow-induced pattern formation in both simple and complex flows (e.g. flow instabilities, product defects), multiscale analysis, and the application of machine learning techniques. About the
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include technique development work aimed at combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow
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also include technique development work aimed at combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a
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work aimed at combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you
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processes. A demonstrated interest in data visualization and large-scale data analysis is highly desirable. The ideal candidate will have a keen interest in understanding complex biological systems
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activities aimed at developing and using biomarker analysis in blood samples for next generation cancer diagnostics. The work will also include studies in basic tumor biology, where we use various in vitro
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial