Postdoc (f/m/d)

at  LeibnizZentrum fr Agrarlandschaftsforschung ZALF e V

The mission of the Leibniz Centre for Agricultural Landscape Research (ZALF) as a nationally and internationally active research institute is to deliver solutions for an ecologically, economically and socially sustainable agriculture – together with society. ZALF is a member of the Leibniz Association and is located in Müncheberg (approx. 35 minutes by regional train from Berlin-Lichtenberg). The institute also maintains locations in Dedelow and Paulinenaue.
The position will be located in the Research Platform “Data Analysis and Simulation” within the Working Group “Multi-Scale Modelling” under the project NEXT-Gen-EXPERT “Next-Generation Environmental and eXtended Tools for Extreme Events & Plant Resilience Assessment” aiming to develop advanced data workflows and algorithms for accessing benchmark dataset including climate data for agricultural research and other domains. The successful candidate will create automated dashboards for data processing, build APIs for natural language interfaces, and leverage large language models (LLMs) to enhance data accessibility for non-technical users.
We offer a full-time position (40 hours per week), limited for 24 months at our location in Müncheberg as
Postdoc (f/m/d)
87-2024

YOUR QUALIFICATIONS:

• PhD in environmental science, agronomy, biology, geography or data science
• experience in agricultural or environmental research is desirable
• expertise in big data integration
• strong experience with Python
• previous experience with systematic reviews and data analysis
• as an internationally research institution, located in Germany we require good to very good written and spoken English skills and the willingness to take part in further training as part of the language courses in German offered internally
• ability to work in a national team

• development of data tools and algorithms for accessing dataset and visualization.
• developing an automated, dashboard for data extraction, data imputation and analysis
• large language models (LLMs) for natural language data query, facilitating data accessibility
• climate data dashboard with advanced extraction, analysis, and visualisation capabilities
• develop an application programming interface (API) for natural language interface for non-technical users to interact with the data repository
• implement LLM API for query processing, result generation and export in defined formats