PostDoc position in Modelling Ecological Restoration of European Forests

Department of Forest Resource Management
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/.

Duties
We are now offering a two year PostDoc position to work as part of the EU Horizon 2020 project SUPERB. The SUPERB project will support the ambitious upscaling of ecosystem recovery targets in the EU Green Deal, including the implementation of ecosystem restoration actions in 12 demo sites across Europe. SUPERB will seek to prove gains in biodiversity assets spurred by these restoration actions through a task on monitoring, reporting and verification of biodiversity ecosystem services (MRV-BES). This task will combine advanced high-resolution remote sensing methods with eDNA sampling and acoustic surveys to describe the condition of forests. The applicant will be central to the completion of MRV-BES at SUPERB demo sites, work within a team with members at SLU, Bangor and Lancaster. The total duration of SUPERB project is 4 years.

We seek an applicant with expertise in ecological modelling and statistical sampling to design the collection of DNA samples at the demo sites, collect drone and ground remote sensing data and carry out acoustic surveys. The candidate will integrate these data sources to model the distribution of biodiversity assets at the demo sites, to help understanding how the integrity of forests can be assessed and how improvements gained from the restoration actions can be evidenced. This is a very multidisciplinary position and thus the expertise required is covered by the supervisors, with proficiency in the fields of forest inventory and remote sensing, genomics, acoustics and modelling ecological communities. Thus, it is expected that the previous experience of the candidate may lack expertise in one or various of these fields, for which training will be provided.

The role involves travelling to the UK to liaise closely with the partners in Bangor and Lancaster. This exciting role includes fieldwork near Umeå, as well as travelling to demo sites in Spain, France, Serbia, Romania, and the Czech Republic. At these demo sites, the PostDoc will develop single-species and community-based ecological models to discriminate between different types of forests at different stages of recovery and predict the expected trajectories of ecological succession. This will improve our scientific understanding of the relationships between ecological integrity and remote sensed characteristics of forests to quantify the success of restoration activities. The PostDoc will also participate in SUPERB project meetings, preparation and presentation of talks, and be responsible for the preparation of MRV-BES reports at SUPERB demo sites.

Qualifications
To be qualified for this position you need a PhD degree in any of the disciplines relevant to the post duties: ecology, biology, environmental science, forest science, remote sensing and/or ecological modelling. Experience in forest ecology, collection of forest data or remote sensing data from drones or otherwise, genomics or DNA sampling, collection and processing of animal acoustic data, and species distribution or ecological community modelling, or any combination of the above are all desirable qualities but none of them an eligibility requirement alone. Preference will however be given to those candidates with experience in modelling ecological communities. The candidate must have demonstrated experience and skills in statistical inference, R programming language and scientific writing. Knowledge about topics such as numerical programming, big data processing analysis (high-performance computing, parallel and/or bash processing), and statistical modelling are desirable qualities. The applicant is expected to be able to take own initiatives, and manage their own work both independently and within the team group. Thus, it is also essential to show good communication skills in English language to liaise effectively with other members of the group in Bangor and Lancaster.

The position is primarily aimed at junior researchers with a PhD that is no more than three years at the end of the application period.

Place of work
Umeå, Sweden

Form of employment
Temporary employment, 24 months.

Extent
100%

Start date
According to agreement

Application
Welcome with your application no later than the 13th of December.



The Swedish University of Agricultural Sciences (SLU) develops the understanding and sustainable use and management of biological natural resources. The university ranks well internationally within its subject areas. SLU is a research-intensive university that also offers unique degree programmes in for example rural development and natural resource management, environmental economics, animal science and landscape architecture.

SLU has just over 3,000 employees, 5,000 students and a turnover of SEK 3 billion. The university has invested heavily in a modern, attractive environment on its campuses in Alnarp, Umeå and Uppsala.

www.slu.se

SLU is an equal opportunity employer.