Research Assistant (f/m/x) and/or Thesis (Master): AI-based Web Scraping for Agricultural Resource Efficiency

Place of work

Leipzig, mobile working partially possible

Working time

up to 19h/ week (research assistant)

Contract limitations

limited contract / 4–6 months, with the possibility to transition into a Master’s thesis project

Contact

Your contact for any questions you may have about the job:
Shekhar Sharan Goyal (shekhar-sharan.goyal@ufz.de); Rohini Kumar (rohini.kumar@ufz.de)

Your application

Please submit your application via our online portal with your cover letter, CV (please omit your photo, age, or marital status) and relevant attachments.

Diversity and Inclusion

The UFZ has a strong commitment to diversity and actively supports equal opportunities for all employees regardless of their origin, religion, ideology, disability, age or sexual identity.
We look forward to applications from people who are open-minded and enjoy working in diverse teams.

The UFZ

The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.

The job

Feeding a growing population without depleting natural resources is a global challenge. This project develops a data-driven framework—enhanced by AI-based web scraping—to benchmark resource-allocation efficiency in multi-cropping systems worldwide using diverse, field-level datasets. Moving beyond single-constraint approaches, it quantifies how combinations of inputs shape productivity and resilience, revealing trade-offs across efficiency, stability, and sustainability. Crucially, the framework links input efficiency to water-quality outcomes (e.g., nutrient runoff and downstream N/P loads) to ensure productivity gains do not compromise rivers, lakes, or aquifers. This project is tentatively funded under the ‘Helmholtz Benchmark Projects – UNLOCK’ program. You will develop AI-based web-scraping pipelines and synthesize metadata to assemble and harmonize field-level agricultural datasets, benchmarking resource-use efficiency across global multi-cropping systems.

Your tasks

• Develop AI-based web-scraping tools to assemble field-level information on resource applications, socio-economic conditions, and environmental stressors—key drivers of yield variability, productivity gaps, and water-quality impacts.
• Conduct metadata analysis for global agricultural and socio-economic datasets
• Familiarize yourself with existing data workflows and modeling frameworks
• Build and apply machine-learning models to assess sensitivity of agricultural productivity to natural and socio-economic variables.

We offer

• Excellent supervision that supports your personal and professional development
• Exciting insights into the work of a leading research institute
• The chance to work in interdisciplinary, international teams and benefit from a wide range of perspectives
• The opportunity to contribute and actively shape your own ideas and impulses
  right from the start
• Modern technical equipment and IT service to optimally support your work

Your profile

• Bachelor’s or Master’s degree in computational science, systems science, agricultural economics, environmental sciences, or related fields
• Strong interest in socio-environmental systems modeling.
• Proficiency in web scraping and programming, preferably using Python
• Additional knowledge of agricultural policies, nitrogen management, and farming practices is an asset
• Please note that we can only consider applications from individuals who are enrolled as students at a German or European university during the contract period.