PhD position in Data Driven Life Sciences

At the Department of Clinical Microbiology, Umeå University, we are announcing the position as a Data driven life science doctoral student in Data-driven epidemiology and biology of infection, which is a fully funded, four-year PhD student position.

Data-driven life science 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 processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven 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. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/

Data driven epidemiology and biology of infection cover research that will transform our understanding of pathogens, their interactions with hosts and the environment, and how they are transmitted through populations. Research will have a strong focus on computational analysis or predictive modelling of pathogen biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health data from pathogen surveillance efforts and biobanks.

The future of life science is data driven. Will you be part of that change? Then join us in this unique program!

About the position

You will be part of the research group of Anne-Marie Fors Connolly at the Department of Clinical Microbiology, Umeå University, in close collaboration with Martin Rosvall at IceLab and Tommy Löfstedt at the Department of Computing Science.

You will be part of the DDLS Research School and contribute to a nationally coordinated, data-driven research environment at the interface of infection epidemiology, clinical microbiology, and artificial intelligence. The project is also embedded in CRITICAL MICROBES, an ongoing effort to centralize and harmonize clinical microbiology data across Sweden for linkage to whole-population registries.

The doctoral student will be part of a creative, cross-disciplinary environment with extensive support for performing the doctoral project.

About the PhD project: CRITICAL AI – Comprehensive Research on InfecTIons Across the Lifespan using AI

The aim of the doctoral project is to develop robust and auditable computational methods for harmonizing nationwide clinical microbiology data and linking them to longitudinal population registries. The project builds on the CRITICAL MICROBES database at Umeå University, where infection test data from Swedish clinical microbiology laboratories are being centralized and linked to patient characteristics and infection outcomes. The data includes electronic referrals, laboratory reports, structured test results, species identifications, susceptibility profiles, interpretative comments, and registry-linked outcome data.

In this project, you will develop and apply AI-based methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen nomenclature, standardize laboratory test methods and result vocabularies, and translate clinical and laboratory free text into structured terminology. The project combines classical text algorithms, medical ontologies, and domain-specific language models, among many other methods and models. You will engage with large-scale longitudinal health data, develop and validate computational models, and contribute to methods that will enable pathogen-resolved population research across the lifespan.

The project is highly interdisciplinary and will provide training in clinical microbiology, infection epidemiology, machine learning, data harmonization, and data science. You will also participate in DDLS training and national networking activities.

Eligibility requirements

Applicants meet the basic eligibility requirements for doctoral studies if they have:

• Completed a second cycle (master’s level) degree,
• Fulfilled course requirements of at least 240 higher education credits (ECTS), of which at least 60 credits at second-cycle level,
• Or otherwise acquired substantially equivalent knowledge in Sweden or abroad (Higher Education Ordinance, Chapter 7, Section 39).

Furthermore, the position is subject to the public service obligations applicable to Swedish civil servants.

Applicants must:

• Have a Master’s degree, or equivalent, in bioinformatics, data science, computer science, computational biology, statistics, public health, biomedical engineering, applied mathematics, physics, or another quantitative field of relevance for the project.
• Have documented programming experience in R, Python or other common programming languages.
• Have experience of quantitative analysis, computational modelling, bioinformatics, machine learning, or another form of data-intensive academic research.
• Have a strong interest in interdisciplinary research, including clinical microbiology, epidemiology, machine learning, artificial intelligence, and health data science.
• Have excellent communication skills in English, both written and verbal.
• Have good time-management and planning skills, and a proven ability to operate independently.
• Have a strong and proven ability to collaborate constructively in a multidisciplinary team.
• Be familiar with the European data protection legislation (GDPR). This is due to the sensitivity of data processed.

Additional merits:

Applicants may also bring the following skills:

• Prior experience with machine learning, natural language processing, biomedical language models, or probabilistic modelling.
• Prior experience with large-scale clinical, laboratory, registry, or other health-related data (experience with Nordic registry data is a plus).
• Familiarity with biomedical ontologies, controlled vocabularies, or coding systems such as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms), NPU (Nomenclature, Properties and Units), ICD (International Classification of Diseases), or similar.
• Experience in statistics, epidemiology, and/or validation of computational models.
• Clinical expertise as a medical doctor with specialization in Public Health Medicine
• Experience of contributing to peer-reviewed publications, preprints, software tools, or other relevant research outputs.
• Experience of pursuing external funding to support research.

Significant emphasis will be placed on cooperative abilities.

Application

The application should include:

• A cover letter, which outlines the applicant’s motivation behind applying for the position and how you fulfill the specified criteria (max 1 page).
• A curriculum vitae (CV), including a list of previous publications, theses, and/or software outputs, where applicable.
• Copies of Bachelor’s and/or Master’s degree certificates, including documentation of completed academic courses and grades. A list of programming courses the candidate has passed should be included separately.
• Documentation of programming training or experience relevant to the project, for example course transcripts, course descriptions, GitHub profile, software portfolio, or equivalent.
• Names and contact details for 2 references.

Applications are submitted electronically via Varbi. Documents should be in PDF format. The deadline for applications is April 30th 2026.

Salary placement follows the established salary scale for doctoral positions. The position is fully funded for four years and starts at the latest October 2026. For more information, contact Anne-Marie Fors Connolly (anne-marie.fors.connolly@umu.se)