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Tobias Sjöblom
6 months ago
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PhD Student in Data-Driven Precision Medicine and Diagnostics (Colorectal Cancer Multi-Omics) at Uppsala University Uppsala University in Sweden
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Expired
Country
Sweden
University
Uppsala University
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Keywords
About this position
The SciLifeLab and Wallenberg National Program for DataDriven Life Science (DDLS) at Uppsala University is recruiting a PhD student for a cutting-edge project in data-driven precision medicine and diagnostics, focusing on colorectal cancer (CRC) multi-omics. The DDLS program is a major national initiative funded by the Knut and Alice Wallenberg Foundation, aiming to train the next generation of computational and data-driven life scientists in Sweden. The program covers four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, and epidemiology and biology of infection.
This PhD position is based in the Department of Immunology, Genetics and Pathology, which has a strong research profile in cancer, autoimmune, and genetic diseases. The department is closely integrated with clinical units at Akademiska sjukhuset, Uppsala, and offers a vibrant research environment with over 100 PhD students and more than 700 affiliated staff.
The project centers on computational analysis of one of the world's largest spatial multi-omics CRC datasets, including whole genome sequencing (WGS), RNA-seq, spatial transcriptomics (Stereo-seq V1), and single-nucleus RNA sequencing (MGI G400, Parse, SmartSeq3) from up to 300 cases. The student will develop and benchmark computational pipelines for spatial and single-cell multi-omics analyses, perform large-scale data integration and annotation, and apply deep learning and Bayesian methods for cell segmentation, spatial domain identification, and tumor microenvironment characterization. Multi-omics integration will link spatial and single-cell findings with genomic, transcriptomic, proteomic, and metagenomic data to identify prognostic and predictive biomarkers.
Applicants must have a Master’s degree in biomedicine or bioinformatics (or equivalent), documented experience in genome, transcriptome, ATAC-seq, spatial and single-cell data analysis, proficiency in HPC (including SLURM, conda/mamba, Snakemake), and strong coding skills in Python and/or R. Experience in cancer research is required. Additional qualifications include expertise in spatial transcriptomics platforms, Bayesian deconvolution, deep learning for biological data, and multi-omics integration. The position is full-time, temporary, and may be subject to security vetting.
Funding is provided as part of the DDLS program, with salary and benefits according to Uppsala University regulations. The start date is August 1, 2026, or as agreed. Applications must be submitted online by May 25, 2026, and should include answers to specific questions in place of a cover letter. For further information, contact Professor Tobias Sjöblom at [email protected].
Join a world-leading research environment and contribute to the future of data-driven life science and precision medicine!
Funding details
Available
What's required
Applicants must hold a Master’s (second-cycle) degree in biomedicine or bioinformatics, or have substantially equivalent knowledge. At least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project worth at least 15 credits, are required. Documented experience in analyses of genome, transcriptome, ATAC-seq, spatial and single-cell data from mammalian tissues or cells at cohort scale is necessary. Proficiency in High Performance Computing (HPC), including practical experience running large-scale jobs on an HPC cluster, CPU large-memory node management, and GPU-accelerated computing, is required. Familiarity with job scheduling (e.g. SLURM), conda/mamba environment management, and pipeline orchestration (e.g. Snakemake) is required. Strong proficiency in Python and/or R for bioinformatics and ability to write clean, reproducible, and well-documented code for complex multi-step pipelines is expected. Documented experience from cancer research is required. Additional qualifications include experience with sub-cellular resolution spatial transcriptomics platforms, Bayesian deconvolution or spatial domain identification methods, deep learning for biological sequence or image data, and multi-omics integration across genomic, transcriptomic, proteomic, and metagenomic data layers. Position may be subject to security vetting.
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Professors
Professor at Uppsala University
Sweden
Research Interests
Biostatistics
Immunology
Cancer Biology
Biomarker Research
Colorectal Cancer
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