PhD Fellow in Computer Science – statistical machine learning / bioinformatics for antibiotic resistance

Deadline: 06.12.2022

UiT The Arctic University of Norway

UiT is a multi-campus research university in Norway and the northernmost university of the world. Our central location in the High North, our broad and diverse research and study portfolio, and our interdisciplinary qualities make us uniquely suited to meet the challenges of the future. At UiT you can explore global issues from a close-up perspective.

Credibility, academic freedom, closeness, creativity and commitment shall be hallmarks of the relationship between our employees, between our employees and our students and between UiT and our partners.

Faculty of Science and Technology

The position

A PhD position in Computer Science is available at the Department of Computer Science affiliated with the Health Data Lab research group and Centre for New Antibacterial Strategies.

The position is for a period of four years. The nominal length of the PhD programme is three years. The fourth year is distributed as 25 % each year and will consist of teaching and other duties. The objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite for employment, and the programme period starts on commencement of the position.

The Department of Computer Science provides an active international research environment with 24 tenured faculty members, 11 adjunct professors, 5 post doctors and researchers, 7 technical/ administrative staff members and about 30 PhD students. The goal of the Department is to advance the research and teaching of computer science as a discipline, to demonstrate leadership within our areas of interest, and to contribute to society through our education, research and dissemination.

The Health Data Lab does interdisciplinary research combining machine learning and experimental computer science with real problems, applications, and data obtained from our biomedical research collaborators. The Centre for New Antibacterial Strategies is a large strategic interdisciplinary centre at UiT for research, education, innovation and dissemination related to antimicrobial resistance.

The position’s field of research

We are looking for a PhD fellow to take an active role in developing statistical-/machine learning methodology and software for the identification, description, and understanding of the molecular processes related to antimicrobial resistance (AMR).

Specifically the candidate will work on developing interpretable methodology for so-called -omic data, eg., gene sequences (DNA), genetic expression (mRNA), and regulators of genetic expression (microRNAs, DNA methylation, etc.). We particularly interested in in applications to bacterial single cell RNA sequencing.

The high-dimensionality of -omic data is a modern problem of increasing importance as our ability to measure things in parallel increases. It is at the methodological forefront, where there is still much need for work. The World Health Organization considers antibiotic resistant bacteria one of the biggest threats to global health, food security, and development today. A better understanding of molecular processes and their relation phenotypes such as virulence factors can help combat AMR and hence yield medical, societal, and economic benefits.

The candidate will work in a multidisciplinary team including colleagues in biostatistics, epidemiology, microbiology and computer science through the Health Data Lab and the Centre for New Antibacterial Strategies.


For further information about the position, contact:

For administrative questions, please contact:


This position requires a Master’s degree or equivalent in Computer Science, Statistics, Machine learning, Bioinformatics or Mathematics. A solid background in statistical-/machine learning or related fields, such as pattern recognition or computational statistics, is in any case required, as well as a decent background in computing/programming, preferably with a familiarity with data analysis environments such as R or Python’s sklearn. Candidates in the final phase of their Master study may apply.

Familiarity with the techniques applied in high-dimensional problems (eg., penalized regression, empirical Bayes, dimensionality reduction) is highly beneficial, but not required. Familiarity with interdisciplinary work, microbiology, and/or -omic data are also beneficial, but, again, not required.

During the assessment, emphasis will be put on the applicant’s potential for research in the described field and personal suitability for the position. We seek a structured candidate with the ability to work independently, who responds well to supervision.

Applicants must document fluency of in English and be able to work in an international environment. Working knowledge of Norwegian or a Scandinavian language is also beneficial.

In the assessment, the emphasis is on the applicant's potential to complete a research education based on the master's thesis or equivalent, and any other scientific work. In addition, other experience of significance for the completion of the doctoral programme may be given consideration.

We will also emphasize motivation and personal suitability for the position.

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.

Admission to the PhD programme

For employment in the PhD position, you must be qualified for admission to the PhD programme at the Faculty of Science and Technology and participate in organized doctoral studies within the employment period.

Admission normally requires:

  • A bachelor's degree of 180 ECTS and a master's degree of 120 ECTS, or an integrated master's degree of 300 ECTS.
  • A master's thesis with a scope corresponding to at least 30 ECTS for a master's degree of 120 ECTS.
  • A master's thesis with a scope corresponding to at least 20 ECTS for an integrated master's degree of 300 ECTS.

In order to gain admission to the programme, the applicant must have a grade point average of C or better for the master’s degree and for relevant subjects of the bachelor’s degree. A more detailed description of admission requirements can be found here.

Applicants with a foreign education will be subjected to an evaluation of whether the educational background is equal to Norwegian higher education, following national guidelines from NOKUT.

If you are employed in the position, you will be provisionally admitted to the PhD programme. Application for final admission must be submitted no later than two months after taking up the position.

Inclusion and diversity

UiT The Arctic University of Norway is working actively to promote equality, gender balance and diversity among employees and students, and to create an inclusive and safe working environment. We believe that inclusion and diversity are a strength and we want employees with different competencies, professional experience, life experience and perspectives.

If you have a disability, a gap in your CV or immigrant background, we encourage you to tick the box for this in your application. If there are qualified applicants, we invite at least one in each group for an interview. If you get the job, we will adapt the working conditions if you need it. Apart from selecting the right candidates, we will only use the information for anonymous statistics.


Your application must include:

  • Cover letter stating specifically which part of your education is most relevant for the position, explaining your motivation and research interests
  • CV
  • Diploma for bachelor's and master's degree
  • Transcript of grades/academic record for bachelor's and master's degree
  • Explanation of the grading system for foreign education (Diploma Supplement if available)
  • Documentation of English proficiency
  • References with contact information
  • Master’s thesis, and any other academic works

Qualification with a master’s degree is required before commencement in the position. If you are near completion of your master’s degree, you may still apply and submit a draft version of the thesis and a statement from your supervisor or institution indicating when the degree will be obtained. You must document completion of your degree before commencement in the position. You must still submit your transcripts for the master’s degree with your application.

All documentation to be considered must be in a Scandinavian language or English. Diplomas and transcripts must also be submitted in the original language, if not in English or Scandinavian. We only accept applications and documentation sent via Jobbnorge within the application deadline.

We offer

  • Involvement in an interesting research project
  • A good academic environment with dedicated colleagues 
  • 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.

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.

A shorter period of appointment may be decided when the PhD Fellow has already completed parts of their research training programme or when the appointment is based on a previous qualifying position PhD Fellow, research assistant, or the like in such a way that the total time used for research training amounts to three years.

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.

We process personal data given in an application or CV in accordance with the Personal Data Act (Offentleglova). According to the Personal Data Act 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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