PhD position in Remote Sensing for Integrative Forest Management

The Department of Forest Resource Management conducts teaching and research in the areas of Mathematical Statistics Applied to Forest Sciences, Forest Remote Sensing, Forest Inventory and Sampling, Forest Planning and Landscape Studies. The Department is also responsible for several environmental monitoring and assessment programs, including the Swedish National Forest Inventory, National Inventory of Landscapes in Sweden, Terrestrial Habitat Monitoring and the Butterfly and Bumblebee Inventory. The work done by the Department combines research and environmental analysis in cross-disciplinary projects, which gives unique opportunities to develop holistic approaches for a sustainable use of the natural resources. At the Department there are approximately 100 employees. For more information visit: www.slu.se/srh.

Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu/

The Department of Forest Resource Management conducts teaching and research in the areas of Forest Remote Sensing, Mathematical Statistics Applied to Forest Sciences, Forest Inventory and Sampling, Forest Planning and Landscape Studies. The Department is also responsible for several environmental monitoring and assessment programs. The Division of Forest Remote Sensing works with developments of remote sensing methods for estimation of forest resources using several kinds of sensor data, for example multi-spectral cameras, laser scanners, and radar sensors. For more information visit: https://www.slu.se/en/departments/forest-resource-management/sections/forest-remote-sensing/ .

We are now seeking a PhD candidate to work on the use of remote sensing for integrative forest management (IFM). The tasks carried out in this PhD work will be synergistic with the objectives of the EU Horizon 2020 projects SUPERB (https://forest-restoration.eu/ ) and TRANSFORMIT (Transforming forest management for multiple ecosystem services and nature conservation via the integrative approach). IFM combines productive forestry and biodiversity conservation by integrating existing practical and scientific knowledge. The PhD work will concentrate mainly in monitoring, reporting and verification of biodiversity ecosystem services, and on the integration of these in forest planning. The work will involve the acquisition and analysis of field and remote sensing data at selected Living Labs which are used to demonstrate IFM. Then this data collected will be integrated into Decision Support Systems (DSS) and forest modelling, to address effects of different forest management approaches on the provision of ecosystem services and effects of climate change on forests. Heureka will be on DSS employed, but also others may be involved as well. Close collaboration with several SUPERB and TRANSFORMIT partners is anticipated. The PhD work improve our scientific understanding of the conservation evidence for IFM approaches and their benefit to nature and the society as a whole. 

To be qualified for this position you need a MSc degree (or equivalent) in forest science and/or ecological modelling (or any discipline relevant to the post), with strong emphasis in remote sensing and/or forest planning. We are searching for a highly motivated and enthusiastic individual with good communication skills, and also team work experience would be a desirable asset. The candidate must be fluent to work in a English-speaking work environment, but also proficient in writing in English in the scientific genre, which can be proved by scientific work written in English (e.g. a MSc thesis or any other published or informal materials). Technical skills should include Geographical Information Systems (GIS) and the statistical R programming environment, with desirable skills in high performance computing (e.g. bash processing, Google Earth Engine, or alike). The candidate should also preferably show experience and skills in the acquisition of field data.  

This position is based in Umeå (Sweden), and may involve travelling to locations in Europe for field data acquisition.

Employment as a PhD student for 4 years.

As soon as possible. By agreement.

Click the “Apply” button to submit your application. The deadline is 2024-01-15.

Please include in your application:

• your curriculum vitae,
• copies of degrees obtained at an internationally recognized higher education institution, plus transcripts of academic records,
• a personally written piece of work that shows your proficiency in scientific writing in English (e.g. a dissertation for master’s or undergraduate degree),
• plus contact information of at least two references familiar with the applicant's qualifications.

Only shortlisted candidates will be interviewed.

Read about the PhD education at SLU at www.slu.se/en/education/programmes-courses/doctoral-studies/

https://internt.slu.se/en/my-employment/employee-associations/kontaktpersoner-vid-rekrytering/

The Swedish University of Agricultural Sciences (SLU) has a key role in the development for sustainable life, based on science and education. Through our focus on the interaction between humans, animals and ecosystems and the responsible use of natural resources, we contribute to sustainable societal development and good living conditions on our planet. Our main campuses are located in Alnarp, Umeå and Uppsala, however, the university also operates at research stations, experimental forests and teaching sites throughout Sweden.

SLU has around 3,000 employees, 5,000 students and doctoral students and a turnover of over SEK 3 billion. We are investing in attractive environments on all of our campuses. We strive to provide a work environment characterised by inclusivity and gender equality, where different experiences generate conversations between people and pave the way for science, creativity and development. Therefore, we welcome applications from people with diverse backgrounds and perspectives.

Contact person

URL to this pagehttps://www.slu.se/en/about-slu/work-at-slu/jobs-vacancies/?rmpage=job&rmjob=9483&rmlang=UK