Ledig stilling på Universitetet i Oslo

Blindern og Urbygningen (Foto: Wikimedia og Colourbox)
Blindern og Urbygningen (Foto: Wikimedia og Colourbox)

Researcher in Machine-learning applied to Macroevolution

Deadline: 07.11.2021

Universitetet i Oslo

The University of Oslo is Norway’s oldest and highest ranked educational and research institution, with 28 000 students and 7000 employees. With its broad range of academic disciplines and internationally recognised research communities, UiO is an important contributor to society.


The Natural History Museum at the University of Oslo is Norway’s most comprehensive natural history collection. For almost 200 years, specimens of animals, fungi, plants, rocks, minerals and fossils have been collected, studied and preserved here. The museum is located at Økern and in the beautiful Botanical Garden, which is not only popular for recreation, but is a scientific collection in itself.

Natural History Museum

Job description

A TWO-YEAR POSITION AS A RESEARCH FELLOW in Machine-learning applied to Macroevolution is available at the Natural History Museum, University of Oslo, Norway.

The research fellow will be part of the project SELECT—Fossil temporal dynamics of phenotypic selection and life history evolution funded by the Research Council of Norway and led by Dr Emanuela Di Martino (PI). The main goal of this project is to apply quantitative genetics models to paleontological data using bryozoans as model system, thus significantly contributing to an integrated understanding of evolution across time-scales.

This two-year position should begin between January and February 2022.

More about the position

The position will be based at the Natural History Museum (NHM), University of Oslo. The appointed researcher will carry out research at the Natural History Museum at its location in the Botanical Garden at Tøyen with the BLEED group. The candidate will work closely with the PI and the project collaborators.

The main goal of the SELECT project is to develop fossilized cheilostome bryozoans as a novel model system to directly link quantitative genetic parameters, measurable fitness components and phenotypic evolution on geological time scales, contributing with unique data and insights to current hot debates on the macroevolutionary nature of phenotypic selection. These objectives will be achieved by compiling data from existing field samples, aided by rapid phenotyping using automated machine-learning algorithms to ensure datasets of sufficient size for robust statistical analyses.

The Research Fellow has the designated task of developing and implementing cutting-edge computer vision tools to extract quantitative phenotypic data from Scanning Electron Microscopy (SEM) images of tens of thousands specimens of fossil and living representatives of related lineages of bryozoans in order to generate highly valuable, solid, quantitative phenotypic datasets.

Qualification requirements

Applicants should be computational scientists with a PhD degree (or other corresponding education equivalent to a Norwegian doctoral degree) in paleobiology or evolutionary biology or, alternatively, a solid and well-documented background in these disciplines. No experience with bryozoans is required, although documented familiarity with the group or groups with similar morphological complexity is an added bonus. The ideal applicant is a motivated and enthusiastic scientist open to collaborative work in our group. They should have excellent writing and programming skills and experience with the development and implementation of computer vision tools to extract high-dimensional high-throughput phenotypic data and to use these in quantitative genetic analyses. Candidates with experience in common software frameworks for computer vision (Keras/ PyTorch) are particularly well-suited for this position. Experience with Python is also desirable.

Applicants must show good interpersonal skills and be willing to work in close collaboration with the project PI and other members of the project team, as well as have the ability to work independently. Applicants should have a good publication record for their career stage. Previous postdoctoral experience is desirable.

A good command of English is required.

Please also refer to the regulations pertaining to the conditions of employment.

We offer

  • salary NOK 553 500 – 594 300 per annum depending on qualifications in position as Researcher (position code 1109)
  • a professionally stimulating working environment
  • attractive welfare benefits and a generous pension agreement, in addition to Oslo’s family-friendly environment with its rich opportunities for culture and outdoor activities

How to apply

The application must include:

  • Application letter including a statement of interest, briefly summarizing your scientific work and interests and describing how you fit the description of the person we seek;
  • CV (summarizing education, positions, and other qualifying activities);
  • Copies of educational certificates;
  • A complete list of publications and unpublished work, and up to 5 academic papers that the applicant wishes to be considered by the evaluation committee;
  • Names and contact details of 2–3 references (name, relation to candidate, e-mail and telephone number).

The application with attachments must be delivered in our electronic recruiting system. Foreign applicants are advised to attach an explanation of the grading system of their university. Please note that all documents should be in English.

Formal regulations

According to the Norwegian Freedom of Information Act (Offentleglova) information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure.

The University of Oslo has an agreement for all employees, aiming to secure rights to research results etc.

The University of Oslo aims to achieve a balanced gender composition in the workforce and to recruit people with ethnic minority backgrounds.

Contact information

For questions regarding the recruitment system: HR-Adviser Thomas Brånå, [email protected]

Apply for position

Powered by Labrador CMS