Master’s Theses: Neuromorphic and Neuro-inspired AI

We are looking for motivated master’s students to explore cutting-edge topics at the intersection of neuromorphic computing, state-space models (SSMs), transformers, and neuroscience-inspired AI. Specifically, we aim to investigate how neuromorphic principles and the entorhinal-hippocampal formation — the brain’s navigation system — can inspire modern machine learning.

The project can be tailored to align with the student's interests and qualifications.

Motivation

- Energy and data efficiency

- Incorporating inductive biases to improve generalization

Project Area I: Neuromorphic State-Space Models & Transformers

- Neuromorphic principles in deep learning

- State-Space Models (SSMs) and Transformers

- Sparsity, quantization, and algorithmic efficiency

- Potential neuromorphic hardware implementation

Project Area II: Neuroscience-Inspired AI (NeuroAI)

- Learning from the brain’s navigation system

- Grid cells, place cells, and cognitive maps

- Spatial representation and memory in ML

- Novel architectures for learning and planning

Your Profile

- Background in computer science, mathematics, physics, or machine learning

- Strong programming skills (PyTorch, JAX, or similar frameworks)

- Excellent analytical and problem-solving skills; self-motivated and proactive

- Enrollment at RWTH Aachen or willingness to conduct the thesis in Aachen (on-site)

What We Offer

- Cutting-edge research at the intersection of neuroscience and AI

- Opportunity to contribute to novel algorithms and potential scientific publications

- Collaboration with experts in machine learning, neuroscience, and neuromorphic computing

- Flexible research directions (theoretical, algorithmic, or hardware-focused)

- Access to compute resources

Application

Please get in touch with a.renner(at)fz-juelich.de to discuss concrete project ideas.

The preferred project duration is March-August 2025, please mention your preferences.

Contact

Dr. Alpha Renner (a.renner(at)fz-juelich.de)

Last Modified: 02.02.2025