Postdoc position (vegetation modeller)
→ Apply before 01/12/2024 (DD/MM/YYYY) 23:59 (Brussels Time)
→ Faculty of Bioscience Engineering
→ Department: BW20 - Omgeving
→ Occupancy rate:100%
→ Number of positions: 1
→ Type of employment: Contract of unlimited duration with clause
→ Term of assignment:
→ Wage scale: PD1 to PD4 (doctoral degree)
→ Required diploma:PhD
Postdoc position (vegetation modeller)
This position is opened at Q-Forestlab (UGent) in the framework of the Belspo research project AFRO-CARDS: “African Forest RecOvery and CArbon Dynamics monitoring through Remote Sensing”.
ABOUT GHENT UNIVERSITY
Ghent University is a world of its own. Employing more than 15,000 people, it is actively involved in education and research, management and administration, as well as technical and social service provision on a daily basis. It is one of the largest, most exciting employers in the area and offers great career opportunities. With its 11 faculties and more than 80 departments offering state-of-the-art study programmes grounded in research in a wide range of academic fields, Ghent University is a logical choice for its staff and students.
YOUR TASKS
Applications are invited for a postdoctoral research position in the field of vegetation modelling at the research group Q-Forestlab (UGent) in the framework of the Belspo research project: “African Forest RecOvery and CArbon Dynamics monitoring through Remote Sensing”.
At least 70% of your assignment will be spent on academic research.
Q-Forestlab (www.q-forestlab.ugent.be) - The laboratory of quantitative forest ecosystem science is a research unit at the Faculty of Bioscience Engineering of Ghent University, Belgium. The research unit is studying vegetation dynamics, carbon and water cycling in terrestrial ecosystems. Q-Forestlab has a broad interest in all types of terrestrial ecosystems, but currently has a strong focus on the ecology of tropical forest ecosystems, especially in DRCongo. Process-based vegetation modelling is the core research tool of the group, but the questions arising from the modelling work require dedicated field work activities. These field work activities are focused on improving uncertain process descriptions within vegetation models and on data-poor regions such as the Congo Basin.
Project - Context: The Congo Basin plays a pivotal role in the global carbon cycle. However, increasing human disturbance due to the huge population expansion is generating large uncertainties in the regional carbon balance. This uncertainty mainly stems from a lack of understanding of forest regrowth trajectories. Objective: A fundamental gain in our comprehension of the recovery of the Congo rainforests and of its functions (functional biodiversity, carbon storage and sink) following slash-and-burn agriculture (here-after referred to as “post-disturbance”), to improve the ongoing and projected changes in the regional carbon balance of African tropical forests. Research hypothesis: Our central research hypothesis is that the recovery rates of carbon and functional diversity in regrowth forests are strongly affected by environment (climate and soil) and land-use history (past disturbances). However, we lack the fundamental understanding of the importance of those drivers to constrain current vegetation models. The lack of understanding these dynamics substantially undermines our capacity to assess and monitor the current and future regional (central African) carbon balance. We urgently need a step-change in our comprehension of the forest functioning post-disturbance, especially in a context of intensifying anthropogenic pressure and under a changing climate. Building this understanding is only possible through integrating forest observations at multiple spatial and temporal scales (from leaf traits to satellite remote sensing), coordinated with the development of Land Surface Models specifically calibrated on those ecosystems. Methodology: The consortium built for this STEREO project combines the unique expertise of multiple partners who have been working for decades on the Congo Basin. It will allow - for the first time - bridging the gap between empirical on-the-ground work, which is critical to understanding the underlying mechanisms, and satellite remote sensing, necessary to upscale and map the observed changes through a detailed proxy-sensing approach based on UAV (drone) monitoring. All those observations will feed Land Surface Models, which are the current gold-standard tool to project the fate of ecosystems in a changing world. Expected outcome: A state-of-the-art monitoring tool and products that assess landscape-level recovery dynamics across the basin; A new reference model for projecting the impacts of climate change and anthropogenic pressure on tropical forest functions.
WHAT WE ARE LOOKING FOR
- You hold a thesis-based doctorate (obtained max. 6 years ago. This term of 6 years is determined by the date written on the above-mentioned required diploma).
- A PhD degree in a relevant field (such as Bio-Engineering, Physics, Geosciences, Environmental sciences, Computer Science Engineering)
- Knowledge of software development is required: fluent in programming with FORTRAN or C++, and postprocessing languages (Matlab, R, python)
- Familiar with the linux/unix environment and handling large (remote sensing) datasets.
- A good command of English, both written and oral demonstrated by good-quality publications in scientific journals and conferences
- Motivation to write high-level scientific publications
- Willingness to collaborate and be a team player with good communication skills
- A keen interest in tropical forest ecology. A background in this field is an asset
- Experience with vegetation modelling, Earth system modelling, or remote sensing is a plus
WHAT WE CAN OFFER YOU
- We offer you a contract of indefinite duration with a maximum term of 2 years.With potential prolongation.
- Your contract will start on 1/01/2025 at the earliest.
- Your remuneration will be determined by salary scale PD1. Click here for more information about our salary scales. Or an international postdoctoral scholarship (both options result in a similar monthly net salary).
- The successful candidate will be based in Ghent, but will have the opportunity to visit partner groups in Europe (e.g. LSCE, France) and North America (e.g. Harvard and Boston University), and to join dedicated field campaigns in Central Africa
- Collaboration in a young and dynamic international scientific team
- All Ghent University staff members enjoy a number of benefits, such as a wide range of training and education opportunities, 36 days of holiday leave (on an annual basis for a full-time job) supplemented by annual fixed bridge days, a bicycle allowance and eco vouchers.. Click here for a complete overview of all the staff benefits.
INTERESTED?
Apply online through the e-recruitment system before the application deadline (see above). We do not accept late applications or applications that are not submitted through the online system.
Your application must include the following documents:
- In the field ‘CV’: your CV and an overview of your study results (merged into one pdf file)
- In the field ‘Cover letter’: your application letter in pdf format.
- In the field ‘Diploma’: a transcript of the required degree (if already in your possession). If you have a foreign diploma in a language other than our national languages (Dutch, French or German) or English, please add a translation in one of the mentioned languages.
- In the field “other documents”: a reference letter, an overview of your study results, …
Note that the maximum file size for each field is 10 MB.
As Ghent University maintains an equal opportunities and diversity policy, everyone is encouraged to apply for this position.
MORE INFORMATION
For more information about this vacancy, please contact Prof. Hans Verbeeck (hans.verbeeck@UGent.be, +32(0)9/264 61 10). Important: do NOT send your application by email, but apply online.
Do you have a question regarding the online application process? Please read the FAQ or contact us via selecties@ugent.be or +32 9 264 34 36