Student assistant (f/m/x) - Leveraging large-language models for agent-based model synthesis, development and documentation

Place of work

Working time

Contract limitations

Julia Kunkel (julia.kunkel@ufz.de)
Christian Klassert (christian.klassert@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.

Your tasks

• Prompt Design and evaluation : Assist in developing effective prompts and evaluation criteria for three steps in the agent-based modelling cycle.

• Model documentation: Test the ability of LLMs to document ABM code in standard protocol formats, e.g., the "Overview, Design concepts, and Details" (ODD) protocol
• Code generation: Test the ability of LLMs to generate ABM code (e.g. in NetLogo or Python) from model descriptions or protocols
• Model extensions: Test the ability of LLMs to synthesize existing ABMs with the aim to identify potential research gaps and model extensions

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

• You are enrolled in a study program such as computer science, data science, cognitive science, computational social/environmental science, or a related field with a strong interest in programming and AI-based tools
• Experience or strong interest in prompt engineering and working with LLMs
• Experience in agent-based modelling and/or socio-environmental systems is a plus
• High level of motivation, independence, and reliability