The Leibniz Centre for Agricultural Landscape Research (ZALF) is inviting applications for a Postdoctoral Researcher position in the BMFTR-funded Junior Research Group “Towards healthy soils by using autonomous field robots in diversified agricultural landscapes” (SoilRob). This research opportunity focuses on the intersection of soil health, autonomous agricultural robotics, artificial intelligence, remote sensing, and sustainable farming systems.

The successful candidate will investigate how autonomous field robots and high-resolution environmental data can improve soil monitoring, strengthen ecosystem services, and support sustainable agricultural production in diversified cropping systems. The position is a full-time research role (TV-L E13) for two years, starting earliest in October 2026, based at the ZALF research area “Land Use and Governance” in Müncheberg, Germany.

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About the Leibniz Centre for Agricultural Landscape Research (ZALF)

The Leibniz Centre for Agricultural Landscape Research (ZALF) is a nationally and internationally recognized research institute dedicated to developing solutions for ecologically, economically, and socially sustainable agriculture. As a member of the Leibniz Association, ZALF conducts interdisciplinary research combining natural sciences, technology, social sciences, and agricultural innovation. The institute is located in Müncheberg, Brandenburg, approximately 35 minutes by regional train from Berlin-Lichtenberg. Additional research locations are maintained in:

• Dedelow, Brandenburg
• Paulinenaue, Brandenburg
• Giessen, Hesse
• Witzenhausen, Hesse
• Geisenheim, Hesse

ZALF provides an internationally connected research environment focused on sustainable land use, agricultural transformation, and climate-resilient farming.

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Research Project: SoilRob – Autonomous Robots for Healthy Agricultural Soils

The SoilRob project explores whether autonomous field robots combined with advanced data technologies can improve soil health and agricultural productivity. The project investigates how:

• Autonomous robotic platforms
• High-resolution environmental sensors
• Machine learning methods
• Digital farming technologies

can contribute to healthier soils, improved ecosystem services, and stable crop yields compared with conventional farming approaches. The postdoctoral researcher will play a central role in developing data-driven solutions that connect field measurements, sensor technologies, and digital agricultural models.

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Main Research Responsibilities

The successful candidate will lead three interconnected research areas.

1. Sensor Calibration and High-Resolution Data Collection

Responsibilities include:

• Calibrating hyperspectral, thermal, and LiDAR sensors mounted on autonomous field robots
• Optimizing sensor performance across experimental agricultural sites
• Generating detailed datasets on soil and plant characteristics
• Developing reliable data acquisition protocols for moving robotic platforms

The researcher will work with advanced sensing technologies designed for precision agriculture applications.

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2. Data Harmonization, Fusion, and Machine Learning

The researcher will develop workflows for managing complex agricultural datasets by:

• Harmonizing heterogeneous data from multiple sensors
• Integrating different spectral ranges, spatial resolutions, and temporal scales
• Combining sensor outputs with field-collected soil health measurements
• Developing reproducible data processing pipelines

Advanced statistical and machine learning methods will be applied to identify relationships between:

• Robot platform effects
• Crop characteristics
• Soil properties
• Soil health indicators

The goal is to generate reliable models for agricultural decision-making.

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3. Digital Farming and Virtual Simulation Development

The researcher will collaborate with the technical design team at TU Dresden to integrate scientific results into the digi.farming.lab virtual environment. Key activities include:

• Implementing simplified crop and soil models
• Supporting scenario development within the Farming Simulator platform
• Translating research data into interactive digital farming applications

This provides an opportunity to combine agricultural science with digital technology and simulation-based learning.

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Key Responsibilities

The postdoctoral researcher will:

• Develop and optimize soil and plant sensing methods
• Process and analyse high-dimensional environmental datasets
• Build machine learning workflows for agricultural applications
• Validate sensor-based models quantitatively
• Work with GIS and remote sensing datasets
• Collaborate with interdisciplinary research teams
• Publish findings in international peer-reviewed journals
• Present results at scientific conferences
• Contribute to future research proposals
• Participate in science communication and transdisciplinary activities

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Required Qualifications

Applicants should have a PhD in one of the following fields:

• Agronomy
• Environmental science
• Geosciences
• Engineering
• Data science
• Related disciplines

Technical Skills and Experience

Candidates should demonstrate:

• Experience with field-based or proximal soil sensors
• Practical knowledge of scientific instrumentation
• Strong Python programming skills
• Experience developing machine learning models
• Ability to process complex environmental datasets
• Experience creating reproducible analytical workflows

Additional desirable qualifications include:

• GIS expertise
• Spatial data analysis experience
• UAV or satellite remote sensing knowledge
• Experience with agricultural data science

A valid driving licence is recommended. Applicants must be willing to travel within Germany for sampling campaigns and collaboration with project partners.

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Salary and Benefits

ZALF offers:

• Full-time employment according to TV-L E13
• 40-hour working week
• Special annual payment according to public-sector regulations
• Company train ticket support
• An interdisciplinary and supportive research environment
• Opportunities for independent scientific development
• Strong networking opportunities within the SoilRob research group
• Supportive work-life balance policies

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Work Location

Institution: Leibniz Centre for Agricultural Landscape Research (ZALF)
Location: Müncheberg, Brandenburg, Germany
Research Area: Land Use and Governance
Working Group: Resource-Efficient Cropping Systems

This position offers a unique opportunity for researchers working at the intersection of:

• Artificial intelligence
• Agricultural robotics
• Soil science
• Environmental sensing
• Sustainable agriculture
• Digital farming

The growing importance of climate-smart agriculture and precision farming creates strong career opportunities for experts who can combine environmental knowledge with advanced computational methods. Researchers completing this project will gain valuable experience applicable to:

• Academic careers
• Agricultural technology companies
• Environmental data science
• Precision agriculture research
• Sustainable food system innovation

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Application Deadline and Process

Reference Number: 32-2026
Application Deadline: July 31, 2026
Expected Start Date: October 2026 or earliest possible date

Applicants should submit:

• Curriculum vitae (CV)
• Proof of academic qualifications
• Certificates and supporting documents
• Other relevant application materials
• Applications should preferably be submitted online.

For email applications:

• Submit one PDF file only
• Maximum file size: 5 MB
• Do not submit ZIP, RAR, or archived files
• Word documents cannot be processed
• To the Online application

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Contact Information

For questions regarding the position: Dr. Adrija Roy Email: Adrija.Roy@zalf.de & Dr. Kathrin Grahmann Telephone: +49 (0) 33432/82-142 Email: Kathrin.Grahmann@zalf.de

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Equality, Diversity, and Data Protection

ZALF promotes equality and welcomes applications regardless of:

• Ethnic, cultural, or social background
• Age
• Religion or ideology
• Disability
• Gender
• Sexual identity

Part-time employment may be possible. Personal data submitted during the application process will be processed according to Articles 5 and 6 of the EU General Data Protection Regulation (GDPR) and deleted after six months unless required for employment purposes.

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Frequently Asked Questions (FAQ)

1. What is the main research focus of the SoilRob project?

The SoilRob project investigates how autonomous field robots and high-resolution sensor data can improve soil health assessment, ecosystem services, and agricultural sustainability.

2. Is this a funded postdoctoral position?

Yes. The position is funded and offered as a full-time TV-L E13 research position for two years.

3. What academic background is required?

Applicants should hold a PhD in agronomy, environmental science, geosciences, engineering, data science, or a closely related discipline.

4. What programming skills are required?

Strong Python programming skills are required, especially for data processing, machine learning development, and automation of analytical workflows.

5. Does the position involve fieldwork?

Yes. The researcher will participate in field sensor campaigns, sampling activities, and collaboration visits within Germany.

6. What technologies will the researcher work with?

The position involves hyperspectral sensors, thermal cameras, LiDAR systems, autonomous field robots, machine learning models, GIS tools, and digital farming simulations.

7. Is experience with remote sensing necessary?

Remote sensing experience is beneficial but not mandatory. Knowledge of GIS, spatial analysis, UAV, or satellite data is considered an advantage.

8. Where is the research position located?

The position is based at the ZALF campus in Müncheberg, Germany, near Berlin.

9. When is the application deadline?

Applications must be submitted by July 31, 2026.

10. Can international researchers apply?

Yes. ZALF welcomes qualified applicants from international backgrounds and provides an interdisciplinary research environment.