The scientific work will be focused on a literature and database survey on the relationships between soil resilience, biodiversity of soil mesofauna and macrofauna, natural disturbances and forest management. Subsequently, the data obtained from these surveys need to be processed by means of formal meta-analytical statistical techniques with an aim to establish general patterns (or the lack of) of the role of soil biodiversity in soil resilience to natural disturbances. The meta-analysis has a dual aim: enhance understanding of the soil biota in ecosystem resilience and use these relationships to improve the KEYLINK and ORCHIDEE models. The successful candidate will work in close collaboration with KEYLINK and ORCHIDEE modelers to ensure that meta-analysis can be used to this aim. This position does not include the modelling work itself.
The position will be supervised by Jorge Curiel Yuste. The successful candidate will be based at BC3 but will be working in a highly multidisciplinary and transnational research environment in close collaboration with Bertrand Guenet (F), Kim Naudts (NL) and Sebastiaan Luyssaert (NL) as part of the H2020 Holisoils project.
Experience/skills required:
Given the interdisciplinary nature of the research we are seeking for a resilient individual with a PhD in for example soil science, soil biology, ecology, forest science, vegetation and soil dynamics or disturbance ecology to mention a few. However, a broad interest in natural sciences more specifically terrestrial ecology is essential. Rather than for a specific training, we are looking for a candidate who is able to demonstrate his/her ability to build a database and conduct a quantitative literature review (meta-analysis) to enhance process understanding. The candidate is expected to have experience with peer review publication and presenting her/his results to an interdisciplinary audience. Hence, good communication skills, as well as ability to work as a part of multidisciplinary and transnational research group are required. In practice, success in the position requires excellent skills in analyzing research data (e.g. R, Python) and ability for independent scientific work proven by a good publication record. A demonstrated ability to communicate in English is also required.
