Posted by Louis Addo | Job


The Faculty of Health Science and Technology has an opening for one full-time post-doctoral research fellow in Biology at the Department of Environmental and Life Sciences in the field of quantitative aquatic ecology, with a focus on fish movement behaviour and machine learning techniques to predict how an eel will move in a river as a function of the surroundings.

The River Ecology and Management Research Group (RivEM), a research environment within the Department of Environmental and Life Sciences at Karlstad University, conducts both basic and applied research in and along rivers and lakes and their surrounding landscapes. The research group is interested in the sustainable use of natural resources in watersheds, working for solutions to environmental problems that benefit both society and nature. Areas of research addressed by RivEM include river connectivity and the effects of hydropower, aquatic- terrestrial interactions and habitats, winter ecology under global climate change, endangered species such as unionid mussels, conservation biology and social-ecological research relating to river regulation and recreational fishing ( Within many of these topics, research is conducted in collaboration with stakeholders from industry, administrative agencies, interest organizations and landowners. You will be employed as a post-doc in Biology and the employment is a temporary full-time position for two years, with a possible one-year-extension, and may include teaching or other academic duties in the Department.


Hydropower dams impact riverine connectivity, deteriorating life-cycle performance of many species as they obstruct the migration routes for organisms between areas used for feeding, reproduction and survival. To prevent further global declines in fish biodiversity, identifying and understanding key fish-environment interactions is crucial for successful conservation strategies. This is especially so for the European eel (Anguilla anguilla) whose population has declined 95% in the last 25 years and is currently categorized as critically threatened. The exact reasons for the decline in the eel population are not known, but a combination of effects from over-exploitation, new pathogens, climatic changes, and habitat degradation including fragmentation are believed to be the most probable causes. Adult seaward-migrating eels are more vulnerable to passage through hydropower installations than many other fish species due to their elongated body length. The need for mitigation and effective strategies for increasing survival of out-migrating eels in regulated rivers is thus obvious.

Concurrently, inferences of cost-effectiveness and relevance of mitigation and restoration efforts demand detailed knowledge of the specific processes that result in elevated migrating fish mortalities. In the case of eels and power plant-induced mortality, there is a very simple solution: prevent the eels from entering the turbines and restore river connectivity. This solution demands knowledge-based development of optimized solutions that should be rooted in in-depth knowledge about eel behaviour and ecology. However, at present there is a lack of detailed knowledge on how to create sustainable solutions to do this and at the same time prevent loss of hydropower electricity production. The reason being a lack of a fundamental understanding on how the eel behaves as a function of the hydrological environment.

Our project aims at developing a statistical framework that provides an understanding of how different key hydrological variables affect eel swimming behavior, and machine learning techniques to predict how an eel will move in a river as a function of the surroundings. The statistical model framework will be developed based on existing models for smolt behaviour, developed by members of the proposed project. This will provide a generic and general understanding of the correlation between hydrological variables and the swimming behavior of eels during downstream migration. This result will then be used in a machine learning model to predict eel downstream migratory routes. The results of this project are expected to help in the development of mitigation solutions for eels to strengthen the European eel population and consequently contribute to the restoration of the ecological dynamics of freshwater aquatic systems.

The successful candidate will work within RivEM, in close collaboration with experts from the Norwegian Institute for Nature Research (NINA) and Vattenfall R&D, with end-to-end data science projects which require leveraging state-of-the-art machine learning techniques, statistical methods, and other advanced analytics tools so as to deliver solutions for fish conservation. Through this role, you will have the opportunity to collaborate and develop your career together with experts within biology and other experienced data scientists in the project. In addition, silver eel telemetry studies in the field to study eel swimming behaviour and hydrodynamics can come into question. The applicant is expected to be active at the university and participate in the research environment.


To be eligible for the position, applicants are required to hold a PhD (or to be completed before the decision about the employment is taken) in quantitative ecology, statistics, computational ecology, or related fields. The candidate must have completed the degree no more than three years before the last date for applications unless special grounds exist. Older PhD degrees can be taken into account when there are special reasons, such as leave due to sick leave, parental leave, clinical service, positions of trust within unions or other similar circumstances. Excellent oral and written communication skills in English are required.

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