Aiding Rule Design in Industry with Machine Learning Theory of Neural Systems


We are currently looking for a bright Master's student interested in applying artificial intelligence to simulate worker behavior for decision-making in an industrial safety context, for a cooperation between the chair for the Theory of Neural Systems and the Department of Work and Organizational Psychology.
Ultimately, this should lead to a meaningful model for rule design and reward framing in real workplaces.

The main focus of the thesis will be the evaluation of reinforcement learning techniques for a provided industry simulation. That includes implementation in the Python programming language.

Phase 1

The student becomes acquainted with the theoretical framework of reinforcement learning (RL), then learns to phrase the given simulation in it, and understands the  conceptual limitations of such an approach.

Phase 2 

The student implements and experiments with different RL-algorithms and explores means to robustly achieve close-to-optimal behavior in the simulation (and possibly variants). This might include overcoming technical difficulties in interfacing with the current implementation of the framework.

Phase 3

A thorough experimental study is conducted to investigate the role of rule design in industrial safety. The study is then interpreted from the perspective of work psychology. The "Department of Work and Organizational Psychology" will guide this evaluation, no working knowledge of the topic is required.

The specifics of the content might change, depending on intermediate results in Phase 2.


  • previous exposure to machine learning techniques, e.g., by successful (80%+) participation in one or more of the INI's lectures on the topic
  • good command of Python or another programming language
  • no fear in the face of applied math
  • psychology background is NOT required for this thesis

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science.

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Tel: (+49) 234 32-28967
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