Missions

In the frame of the HorizonEurope OBSGESSION project, we are looking for a highly motivated postdoc to work on developing and applying state-of-the-art approaches to detect biodiversity changes from remote sensing data and citizen-science/standardized datasets (e.g., GBIF, iNat, eBirds) and attribute these changes to potential drivers. We seek a candidate with strong interests in statistical ecology, remote sensing, artificial intelligence but also on ecological theory to develop usable frameworks and tools (see https://doi.org/10.1098/rstb.2022.0182).

Activités

We are especially looking for someone interested in developing/testing/applying sets of statistical models including machine, deep-learning but also causality approaches to ecological data like distribution data on species, functions and ecosystem services at large spatial scales and using temporal data (e.g., Europe). Candidates should have strong experience in big spatial and temporal data, handling large models and HPCs. Different sources of data (GBIF, INat, TRY, GLOBI) will be jointly used. The candidate also needs to have experience in using and harnessing high spatial and temporal resolution remote sensing data. Interest in causal discovery and inference is more than welcome.
The candidate will have to interact with the other partners of the projects but also with stakeholders to define biodiversity indicators and metrics of interest for them.

Compétences

- A PhD in ecology/biodiversity/geography and/or mathematics/statistics/AI applied to ecological questions.
- A good track-record of publications.
- Proficient in R and Python
- A good experience in analyzing large scale biodiversity data and using statistical models
- Writing and communication skills
- Flexibility and adaptability
- Rigor and autonomy

Contexte de travail

The objective and key ambition of the European Project OBSGESSION is to monitor and predict biodiversity change and its direct and indirect drivers in terrestrial and freshwater ecosystems through the integration of state-of-the-art multi-sensor Earth Observation (EO) data, innovative in-situ (including citizen science) data, and products, together with next-generation ecological models that account for uncertainty.
LECA (https://leca.osug.fr) is part of the Univ. Grenoble Alpes and CNRS in France. Grenoble is set close to some of the most beautiful mountains of the Alps with excellent connections to Lyon and Geneva.
LECA host a large and vibrant community of excellent scientists to interact with, several highly cited researchers, a large variety of experimental and observational cases studies (mostly in the Alps such as ORCHAMP), and several modelling tools already developed (biomod2, FATE-HD, econetwork, metanetwork, VirtualCom, EcoLottery). Working language is English and French.

The selected candidate will join the BIOM team "Describing, understanding and predicting the spatio-temporal distribution and dynamics of biodiversity and ecosystems (BIOdiversity Monitoring), whose research objectives are to study how global changes influence biodiversity, ecosystem functioning and nature's contributions to societies. The team provides an excellent intellectual environment and infrastructure for the research project, with several postdocs and PhD students working on complementary themes.