PhD Position in Inspired Neural Networks: A unique opportunity to pursue a PhD focused on the dynamics and learning processes in artificial and biological neural networks. This position is ideal for those interested in machine learning, theoretical neuroscience, and interdisciplinary collaboration.
PhD Position in Theory of Learning in Artificial and Biologically Inspired Neural Networks
Designation
PhD Candidate (1.0 FTE)
Research Area
- Neural Networks
- Machine Learning
- Theoretical Neuroscience
- Computational Efficiency
Location
Radboud University, Nijmegen, Netherlands
Eligibility/Qualification
- MSc in Physics, Engineering Physics, or Mathematics
- Knowledge of analytical techniques in modeling complex systems
- Familiarity with statistical mechanics methods
- Programming experience (Python, C, Julia)
- Good command of spoken and written English
Description
The PhD candidate will investigate the learning capabilities of neural circuits, focusing on representation transferability, biological constraints, and learning efficiency. Collaborations with other PhD candidates and international partners will be emphasized, alongside opportunities to supervise Bachelor’s and Master’s students and engage in teaching within the Neurophysics Master’s program.
How to Apply
Interested candidates should submit their applications through the university’s application portal. Address your letter of application to Dr. Alessandro Ingrosso, including the required documents as outlined on the application form.
Last Date for Apply
20 October 2025
Table
| Details | Information |
|---|---|
| Position | PhD Candidate |
| Salary | Gross monthly salary: €3,059 – €3,881 |
| Contract duration | 1.5 years (extension possible based on performance) |
| Application Deadline | 20 October 2025 |
| Starting Date | Preferably 1 January 2026 |
| Working Hours | 38 hours per week |
| Teaching Load | Up to 10% of working time |
This is an excellent opportunity for motivated individuals eager to explore innovative research in the intersection of physics, machine learning, and neuroscience.
