Ledig stilling ved Norsk institutt for naturforskning

Three PhD positions on method- and tool development in NINA - preliminary announcement

Deadline: 30.08.2020

NINA has established a new commitment to developing new methods and technology in research and nature monitoring. As part of this, we will employ two doctoral fellows in Environmental DNA (e-DNA) located at NINA Trondheim and one PhD-fellow in machine learning and ecological issues located at NINA Oslo. All three positions will be linked to an acknowledged academic research environment at the Norwegian University of Science and Technology (NTNU).

The Norwegian Institute for Nature Research (NINA) is Norway’s leading institution for applied ecological research, with broad-based expertise on the genetic, population, species, ecosystem and landscape level, in terrestrial, freshwater and coastal marine environments. NINA has 285 employees, our main office is in Trondheim, and we have departments in Tromsø, Bergen, Lillehammer and Oslo.

The final announcement will be published approx. August 10 with application deadline August 30. All three positions must start no later than November-December 2020.


• MSc in biology, ecology, genetics or related fields

• The applicant is expected to document good knowledge in relevant topics for the positions

• The applicant should have basic knowledge in the statistical analysis of data. Experience with the use of the statistical software R

• Fluent in English, written and spoken. Being able to communicate in Scandinavian language is an advantage

PhD position in environmental DNA and DNA metabarcoding related to ecological condition in lakes

The aim of the project is to develop new tools for monitoring lakes based on environmental DNA and DNA metabarcoding, which can be implemented in future monitoring and ecosystem-based management. The project will test sampling and lab methods for DNA-based indices calculated from benthic invertebrate, microcrustacean and fish communities in Norwegian lakes, as well as to describe ecological status of lakes using more taxonomic groups than before. A 3–12 months scientific visit abroad is planned for the PhD fellow. The project will also be able to explore broader freshwater ecological issues.

The PhD position will be located at NINA Trondheim, and associated with the research education program at the Norwegian University of Science and Technology (NTNU) in Trondheim with supervisor from the Department of Natural History.

Contact persons:

PhD position in development of genetic tools for monitoring of wildlife populations

The Norwegian Institute for Nature Research (NINA) invites applicants for a three year PhD-position for development of genetic methods to monitor and study wildlife populations. Candidates should have a background in ecology, population genetics or statistics.

The PhD-candidate will work with development of genetic methods and use of genetic data in analyses of kinship and structuring of wildlife populations. Genetic data will also be used as a basis for work related to estimation of population size using different mark-recapture methods. The research activity of the PhD is directly related to highly relevant issues for population monitoring, management and research of harvested wildlife populations.

The PhD position will be located at NINA Trondheim. The PhD-position will be linked to Centre for Biodiversity Dynamics (CBD; https://www.ntnu.edu/cbd), NTNU, Trondheim, and will follow the PhD-program at NTNU. Candidates should have a background in ecology, population genetics or statistics.

Contact person:

Vebjørn Veiberg, [email protected], +47 957 00 510.

PhD in machine learning related to ecological issues

Machine learning and deep learning are methods that is increasingly being used in ecological research with success. The PhD-candidate will work with development of methods related to image processing, GIS and remote sensing, acoustic environments or soundscapes and environmental-DNA (eDNA).

Image-based recognition has great potential in to automate and improve processes for many applications. Preliminary research in our departments has been carried out to identify plants, individual brown trouts and salamanders from images. This work is only an initial step towards achieving an accurate and automatic system. The PhD-candidate will further develop these methods.

In GIS and remote sensing, Google Earth Engine is arguably the most advanced cloud-based geospatial processing platform in the world. We are increasingly using this platform for processing of large gridded datasets including remote sensing, climate and terrain data. The PhD-candidate candidate will develop standard workflows to integrate Google Earth Engine and TensorFlow for processing of primarily remote sensing data including data from Copernicus Sentinel satellites, orthophotographs, LiDAR datasets and possibly drone imagery.

Monitoring of the acoustic environment, or soundscape, is fast becoming a key tool in species and ecosystem management. Automated acoustical survey methods are needed to scale-up monitoring practices to meet management needs in addressing global change effects on biodiversity. Inexpensive, open access acoustic loggers are now available, paving the way for new approaches to biodiversity monitoring. Yet, analysis methods are still lacking and PhD-candidate will be working on developing methods by machine learning on these issues.

Environmental-DNA (eDNA) is revolutionizing biodiversity monitoring, and the technological development of DNA-metabarcoding is producing high resolution taxonomically data on an unprecedented scale. In this part, machine learning will be utilized for classification of ecological status in relation to various stressors like climate change and anthropogenic effects based on large-scale taxonomically diverse eDNA-data.

The PhD position will be located at NINA Oslo. The PhD-position will be linked to Department of Computer Science (IDI; https://www.ntnu.edu/idi), NTNU, Gjøvik, and will follow the PhD-program at NTNU. Candidates should have a background in ecology, machine learning, image processing and GIS/Remote Sensing

Contact person: