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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
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models. A multidisciplinary approach is expected, integrating microfabrication, cell component and biomaterial incorporation, advanced imaging and staining of specific biological features. You will be
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of human proteins in cell lines using immunofluorescence (ICC-IF) and confocal microscopy. The research group is also applying the latest methods for highly multiplexed immunostaining of cells and tissues
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of cell nuclei We are actively seeking a highly motivated PhD student with a passion for developing experimental methods to join our team. In this role, you will be involved in a groundbreaking project
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communicate on a daily basis with the Head of Unit and Lab Manager. You will also actively participate in technology development with regards to analytical methods and application of workflows to user projects