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, principal component analysis, analysis, analysis of kinship, trait analysis and metagenomic analysis (incl. analysis of pathogenic microorganisms). The bioinformatician is involved in maintaining, updating
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Data Driven Life Science (DDLS). About the DDLS Fellows program Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes
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career development plan. The program integrates international, interdisciplinary, and intersectoral elements to provide a well-rounded educational experience. Networking Opportunities: Build lasting
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life science technologies with data and AI expertise. Computational methods and artificial intelligence applied to large-scale molecular data are transforming the study of biological systems at all
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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, with joint academic–industrial supervision Data-driven life science is a field of research that utilizes data, computational methods, and artificial intelligence to investigate biological systems and
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understand the past, and require specialized laboratories and methods. The purpose of the Ancient DNA unit is to support research by providing state-of-the art analysis as a service to a scientific community
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evaluate them on experimental cryo-EM datasets. Cryo-EM is a rapidly growing field that has transformed structural biology over the past decade. The methods are proven and impactful, yet far from mature
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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on bioinformatics analysis of spatial gene expression data as well as other modalities (i.e. microbiome; metabolites, proteins) generated using the Spatial Transcriptomics (ST) method, Spatial metaTranscriptomics