PhD Fellow in Machine Learning / Statistical Methods for Pathology Images

Deadline: 04.05.2021

UiT The Arctic University of Norway

The Faculty of Health Sciences at UiT The Arctic University of Norway represents a newly created entity gathering health study programs. This facilitates a unique interdisciplinarity and innovation in health education and research. We work closely with the services in the North to solve tomorrow's challenges.

Read more about us at uit.no/helsefak

Faculty of Health Sciences / Department of Community Medicine

The position

A PhD position is available at the Department of Community Medicine, Faculty of Health Sciences affiliated with the research group System Epidemiology in the field of machine learning and statistics.

The workplace is at UiT in Tromsø. You must be able to start in the position in a reasonable time, within 6 months after receiving the offer.

The position is available from August 2021 for a period of four years. The nominal length of the PhD program is three years. The fourth year is distributed as 25% each year, and will consist of teaching and other duties for the Faculty of Health Sciences. The objective of the position is to complete research training to the level of a doctoral degree.

About the System Epidemiology research group

The System Epidemiology research group focuses on population-based research for associations between lifestyles and cancer risk. The group is interdisciplinary with researchers from the field of medicine, biology, statistics, and more. Available data includes gene expression from blood and tumour samples, whole slide images of tumours, questionnaires, and data from population and health registries. The completeness of the data offers unique possibilities to conduct multi-faceted research in a multitude of fields. For further information, see System Epidemiology.

An important aspect of cancer research are the analyses of whole slide images from tumours, and their association with gene expression. Machine learning methods for whole slide image analysis is an active research field with many unsolved challenges, among them, high image resolution, lack of annotation and clinically useful output. A combination of publicly available data and local data and expertise has the potential of solving some of these challenges.

About the project

The project focuses on breast cancer and clinically useful output, in close collaboration with the Department of Pathology (UNN) and the Department of Computer Science. The project offers a unique opportunity to conduct research under the supervision of a multidisciplinary team with expertise in statistics, pathology and computer science.

The PhD candidate will conduct research in the field of machine learning or statistics, applied to whole slide images and corresponding gene expression. Publicly available repositories, such as The Cancer Genome Atlas (TCGA), will be used for development of methods. The Norwegian Women and Cancer Study (NOWAC) (part of the System Epidemiology research group) has data sets suited for application.


Applicants are encouraged to contact:

  • Associate professor Kajsa Møllersen for further information about the position, Phone: +47 97783940, E-mail: [email protected]

We offer

  • Supervision from experienced researchers
  • An interdisciplinary group of PhD candidates
  • A large degree of independence
  • Opportunities for interdisciplinary collaborations
  • Flexible working hours and a state collective pay agreement
  • Pension scheme through the state pension fund

More practical information for working and living in Norway can be found here: Welcome to UiT!


Master’s degree in statistics/computer science/mathematics/bioinformatics or similar with an emphasis on machine learning is required before commencement in the position. Master students finishing their thesis before July 1st can apply. You must document completion of your degree before commencement in the position. Programming skills are required.

Emphasis will be put on the candidate's potential for research and personal suitability for the position. We seek a structured candidate with the ability to work independently and who responds well to supervision.

Admission to a PhD programme is a prerequisite for employment, and the programme period starts on commencement of the position. The PhD candidate will participate in the faculty’s organized research training, and the PhD project must be completed during the period of employment. Documented knowledge of English as stated here is also required and working knowledge of Norwegian or a Scandinavian language is desirable. Information about the application process for admission to the PhD programme, application form and regulations for the degree of Philosophiae Doctor (PhD) are available at: PhD - Faculty of Health Sciences.


The application must be submitted electronically via www.jobbnorge.no and must include:

  • Cover letter specifically stating which part of your education is most relevant for the position
  • CV
  • Diplomas, diploma supplements and transcripts (all degrees)
  • Documentation of English proficiency.
  • Documentation of programming skills (e.g., github repository or similar)
  • Contact information for 2 references (main supervisor for students)
  • Master's thesis (one-page project summary for students), and any other academic works

The documentation has to be in English or a Scandinavian language.

General information

The appointment is made in accordance with State regulations and guidelines at UiT. At our website, you will find more information for applicants.

As many as possible should have the opportunity to undertake organized research training. If you already hold a PhD or have equivalent competence, we will not appoint you to this position.A shorter period of appointment may be decided when the PhD Fellow has already completed parts of their research training programme.

Remuneration for the position of PhD Fellow is in accordance with the State salary scale code 1017. A compulsory contribution of 2 % to the Norwegian Public Service Pension Fund will be deducted.

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background. UiT will emphasize making the necessary adaptations to the working conditions for employees with reduced functional ability.We process personal data given in an application or CV in accordance with the Personal Data Act (Offentleglova). According to Offentleglova information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure. You will receive advance notification in the event of such publication, if you have requested non-disclosure.

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