PhD student in Data-driven precision medicine and diagnostics Full-time NEW

PhD Student in Data-driven Precision Medicine and Diagnostics

Uppsala University, Department of Immunology, Genetics and Pathology

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 processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for DataDriven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden. The program is funded with a total of 3.3 billion SEK over 12 years from the Knut and Alice Wallenberg (KAW) Foundation. In 2026 the DDLS Research School will be expanded with the recruitment of 25 academic and 7 industrial PhD students. During the course of the DDLS program more than 260 PhD students and 200 postdocs will be part of the Research School. The DDLS program has four strategic research areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection.

Data-driven precision medicine and diagnostics covers data integration, analysis, visualization, and data interpretation for patient stratification, discovery of biomarkers for disease risks, diagnosis, drug response and monitoring of health. The precision medicine research is expected to make use of existing strong assets in Sweden and abroad, such as molecular data (e.g. omics).

The Department of Immunology, Genetics and Pathology at Uppsala University has a broad research profile with strong research groups focused on cancer, autoimmune and genetic diseases. A fundamental idea at the department is to stimulate translational research and thereby closer interactions between medical research and health care. Research is presently conducted in the following areas: medical and clinical genetics, clinical immunology, pathology, neuro biology, neuro-oncology, vascular biology, radiation science and molecular tools. Department activities are also integrated with the units for Oncology, Clinical Genetics, Clinical Immunology, Clinical Pathology, and Hospital Physics at Akademiska sjukhuset, Uppsala. The department has teaching assignments in several education programmes, including Master Programmes, at the Faculty of Medicine, and at the Disciplinary Domain of Science and Technology. The department has a yearly turnover of around SEK 500 million, out of which more than half is made up of external funding. The staff amounts to approximately 345 employees, out of which 100 are PhD-students, and there are in total more than 700 affiliated people.

Colorectal cancer (CRC) is the third most frequently diagnosed cancer worldwide. Its extensive molecular heterogeneity, shaped by genetic alterations, tumor microenvironment (TME) interactions, and the gut microbiome, drives variable patient outcomes and limits treatment response. Despite rapid expansion of single-cell atlases, their translational relevance is constrained by the lack of spatial context, matched multi-omics data, and long-term clinical follow-up.

The Sjöblom group has performed the largest multi-omics characterization of CRC to date, encompassing WGS and RNA-seq of 1,063 patients Building on this foundation, sub-cellular spatial transcriptomics (Stereo-seq V1) and single-nucleus RNA sequencing (MGI G400, Parse, and SmartSeq3) data are currently being generated from up to 300 cases. This cohort will constitute one of the largest spatial multi-omics CRC datasets in the world.

The PhD student will join an active project and work on computational analyses of this unique cohort. The work encompasses:

• Spatial transcriptomics pipeline development: mapping, spatially aware QC, normalization, cell segmentation integrating H&E imaging and transcript density, and spatial domain identification
• Single-nucleus integration and annotation: large-scale multi-sample batch integration (up to 3 million cells) using deep learning-based approaches, hierarchical clustering, and cell type annotation benchmarked against published CRC atlases
• Deconvolution and TME characterization: Bayesian hierarchical deconvolution of spatial bins using matched snRNA-seq reference, cell-cell communication inference, and spatial niche identification
• Multi-omics integration: linking spatial and single-cell findings with existing WGS, bulk RNA-seq, plasma proteomics, epigenomic and metagenomic data from the same patients to identify prognostic and predictive biomarkers

Specific research duties include:

• Design, implementation, and benchmarking of computational pipelines for spatial and single-cell multi-omics analyses
• Analysis and interpretation of large-scale datasets from up to 300 CRC cases across multiple data modalities
• Manage HPC resources and job scheduling on NAISS Arrhenius CPU and GPU partitions

To meet the entry requirements for doctoral studies, you must:

• hold a Master's (second-cycle) degree in biomedicine or bioinformatics
• have completed 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, or
• have acquired substantially equivalent knowledge in some other way.
• have documented experience from analyses of genome, trancriptome, ATAC-seq, spatial and single-cell data from mammalian tissues or cells at cohort scale
• have documented proficiency in High Performance Computing (HPC). Practical experience running large-scale jobs on an HPC cluster, including CPU large-memory node management and GPU-accelerated computing. Familiarity with job scheduling (e.g. SLURM), conda/mamba environment management, and pipeline orchestration (e.g. Snakemake) is required.
• have strong proficiency in Python and/or R for bioinformatics and ability to write clean, reproducible, and well-documented code for complex multi-step pipelines.
• have documented experience from cancer research.

Additional qualifications:

• Documented experience with sub-cellular resolution spatial transcriptomics platforms (Stereo-seq, Visium HD, Xenium, CosMx)
• Documented experience with Bayesian deconvolution or spatial domain identification methods
• Documented experience with deep learning for biological sequence or image data (transformer architectures, vision transformers, graph convolutional networks)
• Documented experience with multi-omics integration across genomic, transcriptomic, proteomic, and metagenomic data layers

The employment is a temporary position according to the Higher Education Ordinance chapter 5, §§ 1-7 . Scope of employment 100 %. Starting date Aug 1, 2026 or as agreed. Placement: Uppsala.

For further information about the position, please contact: Professor Tobias Sjöblom, +46184715036, tobias.sjoblom@igp.uu.se.

Please submit your application by May 25, 2026, UFV-PA 2026/885.

Uppsala University is a broad research university with a strong international position. The ultimate goal is to conduct education and research of the highest quality and relevance to make a difference in society. Our most important asset is all of our 7,600 employees and 53,000 students who, with curiosity and commitment, make Uppsala University one of Sweden's most exciting workplaces.

The position may be subject to security vetting. If security vetting is conducted, the applicant must pass the vetting process to be eligible for employment.