AI-generated illustration of the experiment (created with ChatGPT-5.2, 2026).
Responsible co-supervisor: Sara Ershadmanesh
Formal supervisor: Peter Dayan
Supervising Collaborator: Dominik Bach (Bonn)
The Department of Computational Neuroscience at the Max Planck Institute for Biological Cybernetics focuses on building and testing theories and computational models of neural processing, with a particular emphasis on decision-making, learning, and representation.
In dangerous or unpredictable environments, humans must decide whether to keep collecting rewards (e.g., food) or to seek information that helps them stay safe. In this project, you will analyse how people plan under threat and how they trade off explicit reward against information-seeking for safety. We have build a scenario in which biological agents forage for food and must stay clear of threats. We have also collected data from humans performing this task in virtual reality. The goal of the project is to develop computational models based on Partially Observable Markov Decision Processes (POMDPs). These models characterize belief formation about a threat‘s distance and speed, and how these beliefs guide planning in this situation. We will study both optimal behavior and heuristics computational strategies that may explain individual differences in this experiment.
What is available
- Behavioral dataset from a VR experiment
- Collaboration with researchers at the University of Bonn (VR expertise and behavioral analyses)
- A model description and initial codebase
- The active and rich environment at the Computational Neuroscience lab (supervised by Peter Dayan), where you are expected to participate in group activities as well as pursue your Master’s project.
Student tasks
-  10–16 hours per week, scheduled flexibly to fit your
availability - Improve and clean existing code
- Extend POMDP model to generate optimal behavior
- Compare model predictions to human behavior
- Writing the report about project’s analyses and results
Qualifications/Experience
- Strong programming skills are essential (Python or R)
- Knowledge of POMDPs and experience implementing related models (more experience is a plus)
How to apply
- If you are interested about this project, please email the following to sara.ershadmanesh@tuebingen.mpg.de at your
earliest convenience:
CV - Short description of your experience with POMDPs
and computational modeling - Names and email addresses of one-two referees
The Max Planck Society values diversity and gender equity, welcomes applications from all backgrounds (especially underrepresented groups), and aims for a fair hiring process—so applicants should not include a photo in their application documents.Â