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 aims for excellent and integrated research with societal impact. The institute is a member of the Leibniz Association and is located in Müncheberg . It also maintains research stations in Dedelow and Paulinenaue.
With its Tenure Track System, ZALF aims to recruit excellent scientists with essential expertise by providing a guaranteed transitioning to a permanent contract subject to successful evaluation.
The position will be located in the Research Area 4 “Simulation and Data Science” within the Working Group (WG) “Dimensionality Assessment and Reduction”. The WG aims at developing and testing innovative tools for the analysis of environmental and agronomical data in complex settings.
We are offering at our location in Müncheberg a full-time position as
Tenure Track Scientist (m/f/d) on “Landscape Diagnostics merging Theory and Data-Driven Approaches” [Reference N° T02-2025]
T02-2025
Call description
This Classic Tenure Track Call invites applications of scientists with a focus on development and testing of powerful data-driven landscape diagnostic tools for the statistical analysis of complex, high-dimensional relationships in comprehensive landscape research data sets. Sustainable use of landscape resources balancing competing demands need to be identified and numerous cause-and-effect relationships need to be disentangled at the landscape scale.

THE SUCCESSFUL CANDIDATE WILL:

• Implement new, develop further and test powerful data analysis approaches for comprehending high-dimensional interrelationships between numerous variables integrating multiple data sources
• Contribute to developing theoretical foundations of landscape research
• Closely collaborate with colleagues from other science disciplines
• Visualize and communicate complex facts to non-scientific stakeholders
  The call is addressed to dynamic and highly motivated researchers, preferably early-career researchers, holding a PhD degree in environmental, agronomy, or data sciences. We are looking for independent, out-of-the-box thinking scientists. An understanding of or willing to familiarize with advanced approaches of non-linear dimensionality reduction, time series and spectrum analysis, dynamical systems theory, and machine learning are indispensable prerequisites. Experience with environmental or agricultural research in a multi-disciplinary context would be highly advantageous. The candidates need to demonstrate their capacity to independently carry out research through an excellent publication record, successfully acquired competitive third-party funding, and international networking. Previous experience in interdisciplinary analysis and proven inter-disciplinary communication skills are highly desirable.

Please refer the Job description for details