4 Postdoc position for Agriculture x AI in Germany


The Leibniz Centre for Agricultural Landscape Research (ZALF) is looking for four Postdoctoral researchers for their KIKompAg project, which is a combination of agriculture and artificial intelligence (AI). This project will address critical challenges in agricultural research such as monitoring agroecosystems at different scales, by integrating AI into the current agricultural toolbox. In this blog, we will discuss the benefits, available levels of study, financial support, eligibility criteria, and how to apply for this position.

About the Position

The KIKompAg project aims to develop a coherent approach for integrating multimodal data, AI, and simulation methods to characterize agricultural systems across scales. The four postdoc researchers' positions will include artificial intelligence in agricultural science, grassland modeling x AI, remote sensing data fusion x AI, and soil science x AI.

Benefit of this position

The position will offer the opportunity to work on an innovative project that aims to revolutionize agriculture. The researchers will gain invaluable experience by working with AI experts and researchers in the agriculture industry. The project will also provide an opportunity to work with state-of-the-art remote and close sensing products, deep learning, and mechanistic models, and diverse surface and subsurface reference datasets.

Available Level of Study

The four postdoc positions require a Ph.D. in a related field. The positions are available at the postdoctoral level, which is the highest academic level achievable.

Financial Support

The positions are full-time and will be temporarily limited until September 30, 2025. The researchers will receive financial support during this period, subject to funding.


To apply for the positions, candidates must hold a Ph.D. in a related field. They must have a proven track record of research excellence and experience in the application of AI in the agriculture industry. The researchers must have experience with remote sensing, grassland modeling

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