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microenvironments. To achieve that we use state-of-the-art single-cell RNA-seq, multiome, and spatial transcriptomics data generation combined with computational analysis to establish principles of blood lineage
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ATAC-seq, single-cell RNA-seq, spatial gene expression, and whole-genome sequencing (with long reads) data. The candidate will get the opportunity to explore new analysis methods using deep learning
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Nordic Foundation. You will join a vibrant research team led by Prof. Jakob Skou Pedersen that uses generative AI for cancer genomics and single-cell data analysis. Department of Clinical Medicine As a
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students and contributing to educational initiatives. Experience with remote sensing, GIS tools, and image analysis techniques is an advantage, as is knowledge of genetic methods (e.g., SNP-based data
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Work closely with our team on data analysis and interpretation Collaborate with the PI and team on publications, presentations, and potential grant applications Contribute to lab routines and mentorship
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are influenced by different microenvironments. To achieve that we use state-of-the-art single-cell RNA-seq, multiome, and spatial transcriptomics data generation combined with computational analysis to establish
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advantage, as is knowledge of genetic methods (e.g., SNP-based data analysis). Application deadline: 31 may 2025 at 23:59 hours local Danish time Please see the full call, including how to apply
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mentoring students. Qualification requirements The ideal candidate has: A PhD in bioinformatics, computational biology, biostatistics or metabolomics Proven expertise in omics data analysis A history of high
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collaboration between the university and hospital to foster innovative, interdisciplinary research that bridges basic science and clinical practice. Training in state-of-the-art technologies and data analysis as
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. Methodological Skills: Prior experience with NVivo software for qualitative data analysis is desirable. Theoretical Expertise: Familiarity with the socio-technical imaginaries framework or related theoretical