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Umeå University

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PhD position in Molecular Biology in Data-driven Epidemiology and Biology of Infection Umeå University in Sweden

Degree Level

PhD

Field of study

Computer Science

Funding

Funded PhD position within the DDLS Research School; DDLS is funded with 3.3 billion SEK over 12 years by the Knut and Alice Wallenberg Foundation.

Deadline

Nov 19, 2026

Country flag

Country

Sweden

University

Umeå University

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Keywords

Computer Science
Epidemiology
Molecular Biology
Biomedical Engineering
Biology
Algorithm Design
Computational Biology
Pathogen Biology
High Performance Computing
Statistics
Bacterial Genomic
Bioinformatic
Programming Language
ML

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About this position

This PhD project at Umeå University sits at the intersection of molecular biology, bioinformatics, and computer science, within the Department of Molecular Biology and the national DDLS Research School in Data-driven Epidemiology and Biology of Infection.

The student will work on developing computational methods and software for analysing complex biological data generated by the group’s single-cell multiomic approaches. The research focuses on microbial genomes and bacterial communities across different environments, with the long-term aim of building a Microbial Cell Atlas for deeper analysis and visualization.

The project is strongly software- and data-oriented. The team develops tools primarily in Rust, with supporting components in R and Python, and builds web functionality in Rust/web assembly. The work spans preprocessing, genome assembly, imputation, data filtering, DNA function prediction, compression, indexing, and visualization for single-cell whole-genome sequencing and metagenomics data.

The position is embedded in an interdisciplinary environment involving the Department of Molecular Biology, the Laboratory for Molecular Infection Medicine Sweden (MIMS), the Umeå Centre for Microbial Research (UCMR), the DDLS network, and access to the Wallenberg AI, Autonomous Systems and Software Program (WASP). The project is designed for a student who wants to help solve open research problems in scalable bioinformatics and contribute tools that support pathogen tracking, antimicrobial resistance research, and other applications in microbial genomics.

The advert emphasizes independent thinking, flexible project direction, and collaboration across chemistry, microbiology, computing science, mathematics, and data science.

Funding details

Funded PhD position within the DDLS Research School; DDLS is funded with 3.3 billion SEK over 12 years by the Knut and Alice Wallenberg Foundation.

What's required

Applicants must hold a degree equivalent to a European Master’s degree or have completed at least 240 credits, including at least 60 credits at second-cycle level, or have equivalent knowledge. For doctoral education in molecular biology, at least 90 credits in a relevant subject area are required. Proficiency in oral and written English is required. The ideal candidate has a background in Computing Science, Bioinformatics, or a related field combining biology interest with strong programming experience. Required or highly desired experience includes a statically typed programming language and knowledge of algorithm design and common data structures. Advantageous qualifications include experience with Rust, bioinformatics (especially de novo assembly, single-cell analysis, and microbial bioinformatics), web development and dynamic websites, mathematics such as linear algebra, probability, statistics, or stochastic processes, program development, R and Python, machine learning, and any knowledge of biology. Applicants should be highly motivated, able to work independently and in a team, and have excellent communication skills; personal suitability is important.

How to apply

Apply through the university recruitment system by 2026-11-19. Include a CV, degree certificates with courses and grades, a cover letter with research interests and motivation, a link to code on GitHub or equivalent (or a motivation if code cannot be shared), and contact details for two to three references.

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