PostDoc on Developing New Hydrological Maps Using Artificial Intelligence

PostDoc on Developing New Hydrological Maps Using Artificial Intelligence

SLU - Swedish University of Agricultural Sciences - Department of Forest Ecology and Management

Umeå, Sweden

The Swedish University of Agricultural Science, the Department of Forest Ecology and Management, is seeking outstanding candidates for a postdoc position in hydrological mapping with a focus on Artificial Intelligence. The project is aimed at reducing leakage of unwanted substances to the Baltic Sea and the research group is led by PI associate professor Anneli Ågren. The project is a collaboration between several projects linked to environmental monitoring and collaboration with agencies such as Swedish Geological Survey and the Swedish Forest Agency.

Project background and aims:

Forest management carried out close to streams and lakes, may increase the export of sediments, mercury and nutrients to downstream environments. Buffers and machine free areas near streams and lakes are commonly used to protect surface waters and mitigate excess leaching of unwanted substances to the sea. However, implementing these protective measures in practice can be complicated due to insufficient planning basis. The poor representation of the small scale hydrology on maps is a worldwide problem. In Sweden, for example, 64 % of wet areas in the forest and >80% of running waters are missing on current maps (1:12 500). With new better maps the planning of off-road driving, bioenergy extraction or design of forest buffers can be improved and thereby reduce the export of unwanted substances. In a pilot study we generated a machine learning model that could produce more accurate maps (84% accuracy). This Post Doc project aims at scaling up the mapping from demo areas and start testing and implementation on a national scale.

Duties:

The main aim of the project is to develop the next generation of hydrological maps (for stream networks and soil moisture) by combining high resolution digital elevation models with machine learning.

Qualifications:

The project is interdisciplinary in its nature and the doctoral degree could be in soil science, computer science, biostatistics or a subject relevant for the position.

Experience with the following topics is considered a merit:

1) Geographical information systems, 2) Demonstrable knowledge with programing mainly Python and R, 3) Experience with Machine Learning techniques, 4) Good statistics or math skills. It’s a merit to able to show good collaboration skills as well as working independently. Since research is conducted in an international research environment, the ability to collaborate and contribute to teamwork, and a very good command of the English language, both written and spoken, are key requirements.

A post-doc position is part of the career-path for junior researchers, therefore we are mostly searching for candidates with a doctoral degree less than three years old (excluding parental leave).

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© EuroJobsites 2019

EuroJobsites is a registered company number: 4694396 VAT number: GB 880 9055 04

Registered address: EuroJobsites Ltd, Unit 8, Kingsmill Business Park, Kingston Upon Thames, London, KT1 3GZ, United Kingdom

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