Postdoctoral position (2 years) in machine learning for analysis of large-scale, high-dimensional biological data

Umeå University

Application deadline

March 31, 2024

Postdoctoral position (2 years) in machine learning for analysis of large-scale, high-dimensional biological data

Department of Computing Science

  •  Temporary position
  •  100%
  •  Umeå

Umeå University is one of Sweden’s largest higher education institutions with over 37,000 students and about 4,700 employees. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture.

The ongoing societal transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here.

Are you interested in learning more? Read about Umeå university as a workplace

The Department of Computing Science seeks a postdoctoral researcher to develop clustering methods for large-scale, high-dimensional biological data. The employment is full-time, fully funded (salaried with benefits) for two years and starts as soon as possible or by agreement.

Department of Computing science

The Department of Computing Science is characterized by world-leading research in a multitude of areas and is ranked highly in international comparison. The department has been growing rapidly in recent years, with a focus on creating an inclusive and bottom-up driven research environment. To further strengthen our numbers, we are now looking for a postdoctoral researcher in machine learning. Our workplace consists of a diverse set of people from different nationalities, background and fields. If you work as a postdoctoral researcher with us, you receive the benefits of support in career development, networking, administrative and technical support functions, along with good employment conditions. More information about the department is available at:

Our research group in Machine Learning, led by Docent and Associate Professor Tommy Löfstedt, has many years of experience in machine learning, working on both method and theory development and often with applications in e.g., automated radiotherapy or as in this project in the life sciences. Our current research includes e.g., structured regularization, learning with imbalanced data, federated learning, sequence modelling, and generative imaging. The group is growing rapidly and has many ongoing collaborations with both national and international research groups and has active links with both the private and public sectors. The project advertised here is a collaboration with researchers from the Laboratory for Molecular Infection Medicine Sweden (MIMS;, and the SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS; For more information see:

Is this interesting for you? We welcome your application by March 31, 2024.

Project description and working tasks

Our research groups are developing novel methods in which a large number features (i.e., a large number of individual cells) can be profiled using high-throughput sequencing. We are looking for a researcher with an interest in developing novel analysis methods, which are capable of comparing cells and extracting new types of information. A large degree of creative freedom will be provided, and several different datasets are available depending on the researcher’s interests. The focus can be on either the machine learning formulation, or the efficient implementation over the large binary data. Three particular datasets being generated might be of particular interest, and further datasets can be generated upon need.

We are among the first in the world to be able to massively sequence the genomes of individual cells. Cells, however, share a great degree of their genome. By comparing highly similar cells, it will be possible to impute missing portions of the genome. By applying these methods to genomic data derived from the human microbiome, we can track how microbes in the human gut and oral cavity evolve and interact with each other, as well as the human host.

Using another new technology at our disposal, we believe it will be possible to better see which parts of RNA-molecules (genes) are included in which cell. Because genes can have slightly different function due to how RNA is spliced together, this is likely to generate new insights. Cancer cells, in particular, may have greatly altered splicing patterns. Tracking this can aid in finding the most aggressive clones, which drive e.g. a disease.

We are also developing new ways to measure telomere length at the single-cell level. This added readout, which the University Hospital of Umeå excels in measuring, has the potential to give us more sensitive diagnostics for cancer, as well a number of other diseases.

The candidate researcher will be given freedom to explore viable options to extract novel types of information as our new technology permits. Some ideas that may be of interest are (1) new formulations of VAEs, (2) k-mer hashmaps as used for alignment free quantification, and ultrafast fast species comparison, or (3) tools for language analysis. However, we encourage applicants to think outside of the box.

You will present your findings, write manuscripts, help supervise students, and actively foster an interdisciplinary and collaborative research group culture. The project is a collaboration with Johan Henriksson ( at the Department of Molecular Biology, and Laura Carroll ( at the Department of Clinical Microbiology.


Required qualifications:

To be appointed under the postdoctoral agreement, the postdoctoral fellow is required to have completed a doctoral degree or a foreign degree deemed equivalent to a doctoral degree. This qualification requirement must be fulfilled no later than at the time of the appointment decision.

To be appointed under the postdoctoral agreement, 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 prior. If there are special reasons, candidates who completed their doctoral degree prior to that may also be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organisations, military service, or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. Postdoctoral fellows who are to teach or supervise must have taken relevant courses in teaching and learning in higher education.

A qualified applicant is required to have a PhD degree (or a foreign degree that is deemed equivalent) in Computer Science, Mathematics, Statistics, Bioinformatics, or a related subject of relevance for the project.

You should be highly motivated and able to work productively in a team as well as independently. Excellent communication skills for interacting effectively with senior colleagues and peers are required. Proficiency in written and spoken English is also required. Great emphasis will be placed on personal suitability and match to the ongoing projects.

A successful candidate should be familiar with one or more programming languages suited for the project (e.g., the API exposed to bioinformaticians will be written in Python or R; some of the current pipeline for whole-genome sequencing data analysis is written in Rust; SCVI uses PyTorch). We do not expect the candidate to know all these tools or languages, but have sufficient programming knowledge to quickly pick up necessary skills. We expect applicants to come from a diverse range of backgrounds.

Other desirable qualifications:

Experience in sequence analysis (e.g., Transformer models), basic biology, and single-cell analysis are merits but not required.


A full application should include:

  • A cover letter, including a statement of research interests relative to the above topics and a motivation for why your expertise is appropriate for the position.
  • Curriculum vitae (CV) with publication list.
  • Certified copy of doctoral degree certificate or documentation that clarifies when the degree of doctor is expected to be obtained.
  • Certified copies of other diplomas, list of completed academic courses and grades.
  • Copy of doctoral thesis and up to four relevant scientific papers.
  • Contact information to two persons willing to act as references.
  • Other documents that the applicant wishes to claim.

We will ensure that the formal requirements are met and that we have sufficient information to adequately rank eligible candidates.

The application must be written in English. The application is made through our electronic recruitment system. Documents sent electronically must be in PDF format. Log in to the system and apply via the button at the end of this page. The closing date is March 31, 2024.

Further details are provided by Tommy Löfstedt (, Johan Henriksson (, or Laura Carroll (

Last updated: 2024-03-11

Content Responsible: Hampus Persson(