PhD Student in Computational Biology / Bioinformatics

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. One of the core ideas at the department is to stimulate translational research and thus a closer collaboration between medical research and healthcare. Research is conducted in cancer precision medicine, cancer immunotherapy, genomics and neurobiology, molecular tools and functional genomics, neuro-oncology and neurodegeneration, as well as vascular biology. Parts of the operations are also integrated with the departments of oncology, clinical genetics, clinical immunology, clinical pathology, and hospital physics at Uppsala University Hospital. The department has teaching assignments in several program educations, independent courses, and international master's programs within the medical faculty. The department has a turnover of approximately 500 million SEK, of which just over half is external research grants. The number of employees is about 345, of which about 100 are PhD students, and there are a total of over 700 active in the workforce. Please read more about the department's activities here: https://www.uu.se/institution/immunologi-genetik-och-patologi (https://www.uu.se/institution/immunologi-genetik-och-patologi)

Would you like to work on the development of computational methods for gene expression data from single cells (specifically for cellular microenvironments), supported by competent and friendly colleagues in an international environment? Do you want an employer that invests in sustainable employee engagement and offers secure, favorable working conditions? Welcome to apply for a PhD position in computational biology / bioinformatics at Uppsala University.

The laboratory's research focuses on the development of computational methods (statistical methods and machine learning) for data on single-cell gene expression (such as single-cell RNA sequencing, spatial transcriptomics, and in situ sequencing/sequential FISH) to derive complex cellular properties. The research group is linked to the Science for Life Laboratory (SciLifeLab) and is part of the national program for data-driven life sciences (DDLS).

The central focus of the project is on cellular microenvironments, how they can be predicted, how these predictions can provide information about phenotype changes, how they can improve common downstream analyses of single cells, and how they can lead to mechanistic insights within molecular biology and medicine.

The candidate will have the opportunity to individually drive projects forward and develop their own research line as well as expand their skills at the intersection of programming, statistics, and biology.

Responsibilities

The successful applicant will compare, adapt, improve, and develop new data integration tools for spatial and single-cell gene expression data in healthy and cancerous tissues. They will create spatially resolved whole transcriptome maps of the tumor microenvironment at true single-cell resolution and describe their biological properties. They will create software, summarize results for scientific publication, and communicate their research to experts and laypersons.

Qualifications

Eligible for doctoral education is someone who has:

• completed a master's degree in computational biology, bioinformatics, or equivalent, or
• completed at least 240 higher education credits, of which at least 60 higher education credits at the advanced level including an independent project of at least 15 higher education credits, or
• acquired equivalent knowledge in some other way.

Skills at the intersection of programming, statistics, and biology are required. The candidate should have good knowledge of R and/or Python programming and experience in developing original code for the analysis of single-cell transcriptomics data. Experience in advanced statistics, handling large datasets, and data visualization is expected. Fluent written and spoken English is necessary.

Desirable/meritorious skills

Any of the following skills are considered meritorious:

• Experience in handling technical and other biases in data and in managing complex data with interdependent functions
• Experience in HPC, parallelization, and handling large datasets
• Experience in functional inference (e.g., inferring an unobserved external factor through its footprint in data, i.e., transcription factor activity from RNA sequencing data)
• Experience in machine learning, pseudotime, or RNA velocity analyses
• Experience in software development and documentation

Regulations for doctoral students can be found in the Higher Education Ordinance Chapter 5 §§ 1-7 and at https://www.uu.se/medarbetare/organisation-styrning/regler/ (https://www.uu.se/medarbetare/organisation-styrning/regler/)

About the position

The position is temporary, according to HF Chapter 5 § 7. The extent is full-time. Start date xx or by agreement. Place of employment: Uppsala, Rudbeck Laboratory

For inquiries about the position, please contact: Marcel Tarbier, +46737885977, [email protected]

Welcome with your application no later than March 6, 2026, UFV-PA 2025/3213

Uppsala University is a broad research university with a strong international standing. 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 7,600 employees and 53,000 students who, with curiosity and commitment, make Uppsala University one of the country's most exciting workplaces.

Read more about our benefits and what it's like to work at Uppsala University https://uu.se/om-uu/jobba-hos-oss/ (https://uu.se/om-uu/jobba-hos-oss/)

The position may be subject to security clearance. In the case of security clearance, a prerequisite for employment is that the applicant is approved.

We kindly decline offers of recruitment and advertising assistance.

Applications are received in Uppsala University's recruitment system.

Union representatives: Saco-S - [email protected], Seko - [email protected], ST (OFR/S) - [email protected]