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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
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different molecular libraries to examine their effect on the function of regulatory RNAs. Positive hits will then be further analyzed using in vitro translation, DMS MapSeq structural analysis, and other
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dynamical systems theory, including differential equations, simulation techniques, state-space and input-output representations, time-delay embedding, and/or time series analysis from experimental data
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the following fields: mathematics, physics, computational science, or engineering, and have a strong background in numerical analysis and scientific computing. Required qualifications include basic knowledge
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-der-valk/ ). We are looking for someone with a passionate interest in insect phylogeny and evolution, and with strong competence in de novo genome assembly and phylogenomic analysis. The successful
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-of-the-art cell and tumor biology methodologies, tissue analysis with histopathological techniques, and work with genetically engineered mouse models of cancer. Previous experience of conducting and analyzing
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applications, specifically targeting the prognosis and risk prediction of Heart Failure (HF) in patients. This research integrates AI safety, explainability, and multimodal medical data analysis to enhance
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should have experience in method development, and various molecular biology and biochemical analysis techniques. You should also have a strong research track record, as evidenced by first authorship
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programming skills and finite element (FE) analysis experience is a requirement. Knowledge of machine learning, Matlab and Abaqus is also highly desired. Previous experience with parametrization and automation
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model design and analysis as well as statistical model parametrization and validation techniques. This Postdoc position is part of a five-year research program funded by the Wallenberg Foundation, aimed