Postdoctor in machine learning for single-cell data analysis

Umeå University is one of Sweden’s largest institutions of higher education with over 37,000 students and 4,800 faculty and staff. We are characterised by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison. Umeå University is also the site of the pioneering discovery of the CRISPR-Cas9 genetic scissors - a revolution in genetic engineering that has been awarded the Nobel Prize in Chemistry.

At Umeå University, everything is nearby. Our cohesive campus environment makes it easy to meet, collaborate and exchange knowledge, which promotes a dynamic and open culture where we rejoice in each other's successes.

Are you interested in knowing more about Umeå University as a workplace read more at: Work with us.

The Department of Molecular Biology seeks a postdoctoral scientist to work with varational autoencoders to model gene isoforms in single-cell data. The position is funded by the Medical Faculty. The position is full-time and for two years, access by agreement.

The Department of Molecular Biology has about 200 employees. The department's main tasks involve research, graduate education, and undergraduate education as well as interactions with the community. Affiliated with both the Faculty of Science and Technology and the Faculty of Medicine, the department's research and education activity includes several molecular biological areas within science and medicine: cell biology, microbiology, infection biology, tumour biology and neurobiology in conjunction with the use of genetic model organisms (except plants). The department offers a vital research environment with a broad setup of modern imaging, metabolomics, genomics and screening facilities.

Gene isoforms play key roles in disease etiology and outcome. Despite that gene isoforms are involved in most biological processes, they are commonly ignored because we lack tools to understand them. In this project we will build new large-scale machine learning tools to extract gene isoform information from commonly existing single-cell RNA-seq data.

The approach we consider is based on variational autoencoders and sequence models. The challenges lie in the data being extremely large, sparse, and noisy. The project will open doors into the currently most rapidly expanding field in biology.

You will carry out formulation, implementation and testing of machine learning approaches to model and understand single-cell RNAseq data. You will present your findings, write manuscripts, help supervising students, and actively contribute to a collegial lab culture.

You will be based in the lab of Johan Henriksson (www.henlab.org ) in collaboration with the machine learning group of Associate Professor Tommy Löfstedt. The Henriksson lab is based at the Department of Molecular Biology and is part of The Laboratory of Molecular Infection Medicine Sweden (MIMS), which is the Swedish node within the Nordic EMBL Partnership for Molecular Medicine. The project is run in close collaboration with Tommy Löfstedt, docent and associate professor and head of the machine learning group at the Department of Computing Science.

A person who has been awarded a doctorate in relevant subject area or a foreign qualification deemed to be the equivalent of a doctorate qualifies for employment as a postdoctoral researcher. This eligibility requirement must be met no later than the time at which the appointment decision is made.

Postdoctoral researchers who are to teach or supervise must have taken relevant courses in teaching and learning in higher education.

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 also required. Great emphasis will be placed on personal suitability.

A successful candidate should be familiar with Python and at least one framework for deep learning (e.g. PyTorch or TensorFlow/Keras). The candidate should have documented experience in machine learning or mathematical/statistical modelling.

Since appointment as a postdoctoral researcher is a career-development position for junior researchers, we are primarily interested in applicants who completed their doctoral degree no more than three years before the application deadline. If there are special reasons, an applicant who has completed their degree earlier than that may be considered. Special reasons include absence due to illness, parental leave, clinical practice, appointments of trust for a trade union organisation or other similar circumstances, and for relevant duties/assignments within the subject area.

Experience in basic biology, and especially sequence analysis, or experience in single-cell analysis is a merit.

The appointment is a full-time, fixed-term position for two years in accordance with the terms of contract for fixed-term employment as a postdoctoral researcher. Access as soon as possible or by agreement.

A full application should include:

• An introductory letter including a 2-page statement of research interests relative to the above topics and a motivation of why your expertise is appropriate for the position
• Curriculum vitae (CV) with publication list
• Certified copy of doctoral degree certificate and other relevant degree certificates
• Copy of doctoral thesis and relevant scientific papers
• Contact information for at least two reference persons

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 Swedish or English. The application must be made via our e-recruitment system Varbi and must be received latest 1st Deccember 2022.

You can find more information about us at www.mims.umu.se and www.ucmr.umu.se . For questions regarding the position, please contact the project supervisors.

For more information about The Department of Molecular Biology please visit our web page www.umu.se/en/department-of-molecular-biology or for The department of Computing science, https://www.umu.se/en/department-of-computing-science/ .