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. The project explores the role of tumor-promoting inflammation in cancer progression through bioinformatics-driven, machine-learning and multi-omics analyses integrated with experimental data. Ideal candidates
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research school. Data driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular
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part of an ERC starting grant and involves studying the impact of disease on endosomal properties and the processing of lipid nanoparticles. Key techniques will include omics, cell culture and small
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of large-scale multi-omics cancer data. Proven experience in development of bioinformatics tools and software packages. In-depth knowledge in NGS data processing from whole genome sequencing and RNA-seq
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and bioinformatic analysis of gene expression data in different types of tissues: mice, humans and plants. This work will include analysis of data generated with the latest Spatial Transcriptomics and
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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
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the department, including genomics and transcriptomics, antibody development based on phage display, large-scale mass spectrometry-based proteomics, and associated bioinformatics. The department is host to a unit
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biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life
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knowledge should undergo such training within the first two years of employment. Assessment criteria In the appointment process, special attention will be given to research and teaching skills. The assessment
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. Qualifications The candidate must have: At least masters level education in a relevant field for the application (e.g. structural biology, bioinformatics, biochemistry, molecular biology, molecular genetics