Researcher in Physical Geography by MGeo

Subject description
The position involves research within a three-year research project,
https://portal.research.lu.se/sv/projects/reduced-growth-and-carbon-sink-in-swedish-forest-where-when-and-w/
funded by the Swedish National Space Agency. The project aims to:
1)
build a spatial database based on Sentinel-2 data, climate data, and other geospatial datasets,
2)
estimate primary productivity (GPP and NPP) and respiration for Swedish forest land at a spatial resolution of 10 metres and
3)
test hypotheses that may explain changes in Swedish forest growth and carbon sequestration.
The work will be carried out within MGEO, primarily within our remote sensing research group . We are a dynamic and
Remote sensing | Department of Physical Geography and Ecosystem Science
recognised research environment focusing on optical remote sensing for studies of vegetation, phenology, carbon balance, and their changes over time.
Work duties
Research in forest remote sensing, with a focus on quantifying GPP, NPP, and respiration in Swedish forests during 2016– 2026.
Implementation and application of remote-sensing-based models for estimating GPP, NPP, and respiration in boreal forests.
Collaboration with industry and society on issues related to Swedish forestry.
Administrative duties related to the tasks described above.
Qualification requirements
A doctoral degree in Physical Geography, Forest Science, or an equivalent field.
Research experience in forest remote sensing and estimation of vegetation productivity.
Knowledge of forest ecology.
Excellent spoken and written English.
Good programming skills in Python or an equivalent programming language.
A high degree of independence and good ability to collaborate with others.
Experience of publishing research in international scientific journals.
Assessment criteria and other qualifications
Experience with TIMESAT, DBEST, or similar methods for time-series and change analysis using remote sensing data.
Good spoken and written Swedish.
Experience or knowledge of Swedish forestry.
Experience with Linux and handling large datasets.
Experience with AI-assisted programming.
Experience in modelling forest productivity, including light-use-efficiency models and/or dynamic vegetation models.
Consideration will also be given to the applicant's ability to collaborate, initiative, and independence, as well as the extent to which the applicant's experience and expertise are considered to complement and strengthen ongoing research within the department and contribute to its future development.
Terms of employment
Permanent position with desired start date 2027-02-01 or due agreement, probationary employment might apply. Full-time employment of 100% due LAS § 4 (Lagen om anställningsskydd).
Instructions on how to apply
Applications shall be written in English and be compiled into a PDF-file containing:
The application must be written in English and include:
Curriculum vitae (CV), including a list of publications.
A general description of previous research and future research interests (maximum two pages).
Contact details for at least two personal references.
A copy of the doctoral degree certificate and any other certificates or documentation that the applicant wishes to submit.
A brief written statement explaining why you are interested in the position.
Welcome to apply!