Postdoctoral fellowship - Machine learning methods - Ecosystem acoustics

Application deadline: 10.02.19

Institute of Marine Research

The Institute of Marine Research (IMR) is one of the largest research institutes of its kind in Europe, with approximately 1,000 employees and a wide range of research facilities and laboratories of high international standard. Our main activities are research, advisory work and monitoring. IMR owns and operates six research vessels. Our main offices are in Bergen, and we have a department in Tromsø and research stations in Matre, Austevoll and Flødevigen.

In January 2018 IMR was merged with The National Institute of Nutrition and Seafood Research (NIFES). The new institute will be a leading supplier of knowledge relating to the sustainable management of the resources in our marine ecosystems and the whole food chain from the sea to the table.

About the position

The Institute of Marine Research (IMR) in Norway has a 2 years vacancy for a position as a postdoctoral researcher on machine learning methods. The position is attached to the research group Ecosystem Acoustics and is funded through the COGMAR project. The successful candidate will be part of a team working on methods for analyzing large data sets using modern techniques from machine learning and artificial intelligence, in close collaboration with the other partners in the COGMAR project. A main task will be to develop unsupervised or semi supervised methods for cases where the training data is limited or has variable quality. The work place will be at IMR in Bergen, Norway.

A main mission for IMR is marine research and management of marine resources. We are increasingly relying on data collection using advanced sensor technology (e.g. from broadband sonars, imaging, and advanced laboratory equipment), and the increase in volume and complexity of collected data has turned data analysis into a major bottleneck. Efficient use of the growing data stream will be essential, both for understanding the marine environment and for ensuring best possible advice to managers. In the project, we will develop methods for automating the analysis.


The candidate must hold a doctoral degree or equivalent education in a relevant field, e.g. informatics, physics, applied mathematics, or statistics. Further, the candidate should have:

  • Demonstrated capability of producing scientific publications.
  • Experience in computer programming (e.g. Python or R).
  • Knowledge and experience in machine learning methods
  • Knowledge and experience machine learning frameworks (e.g. ScikitLearn, Keras, pyTorch, Theano, Torch, or TensorFlow).
  • Good command of the English language, both spoken and written.

Other desired factors are:

  • Expertise in marine biology.
  • Experience with supercomputing and/or working with large data sets.
  • Experience with unsupervised and semi-supervised machine learning methods
  • Ability to work independently and in collaboration with others.
  • The working language at IMR is Norwegian. A working knowledge of the Norwegian language will be advantageous, and language training will be offered if necessary.

We offer

  • A positive, challenging and creative work environment.
  • The opportunity to work in a national institute with a high level of international contact.
  • Flexible working hours, and a wide range of welfare services.

The Institute offers a governmentally regulated salary as 1352 postdoctoral fellow and an excellent pension scheme through the Norwegian Public Service Pension Fund, and other welfare benefits (see for more information).

Additional information

For more information about the vacant position, please research group leader Rolf Korneliussen ([email protected]), senior scientists Nils Olav Handegard ([email protected]) or Ketil Malde ([email protected] ) .

You can also find more information on our web site:

IMR is an “Inclusive Working Life Enterprise” concerned with diversity and therefore encourages all qualified candidates to apply. Please note that information about applicants can be made public even if an applicant has requested to be exempted from the list of applicants. Applicants will be notified about this in advance.

Please apply electronically using the link on this page, attaching your CV with documentation to support your application (including a list of publications, transcripts or other documentation).

Please submit your application electronically via the link on this page, enclosing a full CV including a list of publications, copies of relevant recommendations and academic transcripts.

Application deadline: 10.02.19

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