The Department of Clinical Microbiology is offering a postdoctoral scholarship within the project Image analysis of virus distribution in the brain. The scholarship is full-time for two years.
Departmental specific information
The Department of Clinical Microbiology at Umeå University conducts outstanding research and education in host–microbe interactions, disease-causing mechanisms of microbes, and immune system responses. The research encompasses both cellular and molecular aspects of infections caused by bacteria, viruses, and fungi, as well as how the immune system responds to and regulates these processes. The department has around 30 active research groups with different profiles.
The position as postdoctoral fellowship is in the research group of Överby Wernstedt and the research involves studying tick-borne encephalitis virus distribution patterns in the brain. The goal is to understand virus tropism through novel methods in image analysis, image processing, and machine learning when analyzing three-dimensional images of viral infection in mouse brains.
Project description
The project aims to develop novel combinations of image analysis and machine learning methods to analyze light sheet fluorescence microscopy (LSFM) data of infected mouse brains. A central goal is to integrate LSFM data with the MRI-based OCUM brain template to generate quantitative spatial maps of viral infection across the whole brain. This will enable the identification of infected neuroanatomical pathways and provide new insights into how viruses spread in three-dimensional brain space. The project is a collaboration with Associate Professor Tommy Löfstedt at the Department of Computing Science and Doctor Minh Hoang Vu and Doctor Luca Panconi at the Swedish AI Factory.
In the project, you will develop computational pipelines for multi-modal image registration between LSFM and MRI data, including affine and non-rigid registration methods. You will design methods to handle anisotropic voxel data (e.g., z-axis distortion) and perform resampling to enable alignment with the OCUM template. You will also develop methods for 3D image segmentation, artifact detection and removal, and quantitative analysis of viral signals.
In addition, you will develop predictive models of viral spread using machine learning approaches, including graph-based models that incorporate anatomical connectivity priors from the Allen Mouse Brain Connectivity Atlas.
You will independently plan and implement computational methods, take a leading role in pipeline development, and collaborate with researchers across virology, neuroscience, and computational science.
You will communicate and present your research within the university and at national/international conferences and write articles of high scientific quality.
Qualifications
Eligible to be a postdoctoral fellow are those who have completed a doctoral degree, or a foreign degree deemed equivalent to a doctoral degree, in computer science, mathematics, bioinformatics, or equivalent subjects that are considered relevant to the project.
Priority should be given to candidates who completed their doctoral degree, according to what is stipulated in the paragraph above, no later than three years before the application deadline. If there are special reasons, candidates who completed their doctoral degree before that may also be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, or other forms of appointment/assignment relevant to the subject area.It is required that the applicant has strong knowledge in one or more of the following areas:
Image analysis and image processing
Machine learning/deep learning, preferably for volumetric or spatial data
Computational methods for medical or biological imaging
Applicants must have strong programming skills (e.g., Python or similar), experience working with large datasets, be fluent in spoken and written English, and have a high motivation for research tasks.
Experience in the following is considered a merit:
Medical imaging (e.g., MRI, CT, or microscopy data; segmentation, registration)
Image registration methods (e.g., affine and non-rigid registration)
Graph-based modelling or network analysis (e.g., graph neural networks)
Experience with large-scale data processing and high-performance computing environments (e.g., Slurm-managed environments)
Experience from international and interdisciplinary research environments
Since the project involves collaboration, you must also be able to function productively in a group. Excellent communication skills are required to effectively collaborate with colleagues, including colleagues from complementary research areas. Applicants must also have the ability to independently plan, develop methods, perform analyses, and summarize results. The applicant is expected to be able to independently perform advanced analyses of large amounts of high-dimensional data.
Conditions
The postdoctoral fellowship is full time for two years. Starting date by agreement.
Application
A cover letter that clearly describes how you fulfill the specified requirements (include documentation) and your reasons to apply for the position, summarized on one A4 page (in English).
A curriculum vitae (CV) and publication list.
Certified copies of your PhD degree certificate and other relevant degree certificates.
Contact information to at least two references.
Documentation of other relevant experiences or competences.
The application must be written in English. Attached documents in other languages should be translated. Attached documents must be in PDF format.
Applications must be submitted electronically through Varbi, and the last day to apply is May 6th, 2026.
For additional information, please contact Anna Överby Wernstedt (anna.overby@umu.se).
