39 parallel-and-distributed-computing-"UNIS"-"Meta" positions at SciLifeLab in Sweden
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, including high-throughput screening, high-content imaging, omics technologies, and computational approaches, to elucidate mechanisms of toxicity. Ultimately, our work contributes to a deeper understanding of
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). Particular emphasis is placed on HPC-supported computing of sequencing data into assembled transcripts (de novo assembly is a frequent need), and further downstream translation of the ORFs of said transcripts
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to study host-microbiome interactions at the spatial level in the colon. The research activities of the doctoral student will focus on the experimental and computational analysis of spatial gene expression
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information about us, please visit: the Department of Biochemistry and Biophysics . About the DDLS PhD student program Data-driven life science (DDLS) uses data, computational methods and artificial
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project to study genetic regulatory variation and its link to molecular, cellular and organismal phenotypes using a systems genetics approach. The project is fully computational, and potential approaches
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have a background in bioinformatics, computational cancer biology, or related fields with experience in cancer and data-driven research. For more details about our research visit: www.alundberg.org
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these questions through an interdisciplinary lens, with a strong focus on mathematical and computational methods closely connected to evolutionary theory and biological data. Read more about our research themes and
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Lund University, Faculty of Medicine, Department of Laboratory Medicine Lund University was founded in 1666 and is repeatedly ranked among the world’s top universities. The University has around 47 000 students and more than 8 800 staff based in Lund, Helsingborg and Malmö. We are united in our...
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and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting
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. The candidate will combine experimental and computational approaches. The project will start with bioinformatics-driven analysis, followed by integration of data generated from experimental models. Over time, the