Reinforcement Learning (RL) is a versatile and powerful tool for control, but often data-inefficient, requiring numerous updates and non-local information such as replay buffers and batch-based processing. This project will investigate event-driven learning approaches in the context of RL in an event-triggered fashion. Data efficiency will be improved by using meta-learning and pre-training that facilitate few-shot adaptations. The research will show how the complementarity of event-triggered learning and meta-learning can drastically increase RL efficiency at test time.
The co-design of algorithms and digital neuromorphic hardware is an additional avenue for enhancing the efficiency of the methods. In this context the research will explore digital, event-based implementations of the event-based RL learning rule. Benchmarking criteria include accuracy, latency, data efficiency, and energy consumption to reach a learned solution on small robotic control tasks. Your tasks in detail:
- Develop an event-driven RL algorithm that sparsely updates network state and parameters that will significantly improve energy to-solution efficiency compared to conventional digital accelerators when applied to control problems or tiny RL scenarios.
- Explore digital hardware realizations of the proposed RL algorithms within existing spiking neural network chip designs.
- Quantitative comparisons with different hardware backends. Design conventional (GPU-based) deep neural networks for comparison.
- Publish research articles, regular participation in top international conferences to present your work.
- Complete two 6-month internships at TU Delft (Prof. Charlotte Frenkel) and at Mercedes-Benz, Böblingen.
- Participate in yearly retreats organized by the doctoral network participants
- Support the dissemination of software tools and concepts.
- Supervise student projects and BSc/MSc theses.
Table of Content
Summary
Subscribe for Scholarship Alert!
Benefits
- The prestige and career benefits of being a Marie Sk?odowska-Curie Actions (MSCA) Doctoral Fellow, including international visibility, research excellence, mobility opportunities, and competitive salary arrangements.
- A world-leading, interdisciplinary and international research environment, provided with state-of-the-art experimental equipment and versatile opportunities to grow as a curious researcher.
- An interdisciplinary and collaborative work environment including researchers at the following institutes: Neuromorphic Hardware Nodes (PGI-14), Electronics Materials (PGI-7), the Institute of Neuroscience and Medicine - Computational and Systems Neuroscience (INM-6), The Jülich Supercomputing Center (JSC) and the Faculty of Electrical Engineering and Information Technology at RWTH Aachen.
- 30 days of annual leave and provision for days off between public holidays and weekends (e.g. between Christmas and New Year)
- Flexible working hours
- Further development of your personal strengths, e.g. through an extensive range of training courses; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/en/judocs
- You will be enrolled in the PhD program of the department of Electrical Engineering and Information Technology, RWTH Aachen.
- Targeted services for international employees, e.g. through our International Advisory Service
Requirements
- Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field.
- Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must.
- The ability for creative and analytical thinking across discipline boundaries and abstraction levels is a must.
- Knowledge in integrated circuit design, testing and simulation using Cadence is a plus.
- Knowledge of digital neuromorphic hardware and sensors is a plus.
- Ability for collaborative work, interdisciplinary and cross-topical thinking.
- Very good communication skills in English, both spoken and written. PGI-15 offers an English speaking environment, therefore German language skills are not required.
Check also:
Scholarship winning strategies for 2026
Application Deadline
September 19, 2025How To Apply
Are you qualified and interested in this opportunity? Kindly go to
Forschungszentrum Jülich on www.fz-juelich.de to apply
For more information, kindly visit Forschungszentrum Jülich scholarship webpage.
