PhD position to investigate the biophysical basis of neural computation with machine learning



Leuven, Belgium


About the Lab

The Gonçalves lab is a recently founded research group at the Neuro-Electronics Flanders (NERF), Belgium. We are currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning. In particular, we develop machine learning methods to derive mechanistic insights from neuroscience data, and apply them to challenging neuroscience problems: from the retrieval of complex input-output functions of biophysically-detailed single neurons to the full characterisation of mechanisms of compensation for perturbations in neural circuits [1-3]. We work in an interdisciplinary, collaborative, and supportive work environment, which emphasizes diversity and inclusion. 

NERF is a research institute empowered by imec, VIB, and KU Leuven. 

The project

We are looking for a Ph.D. candidate interested in using computational methods to investigate how the complex biophysics and morphology of single neurons give rise to the dynamics of neural populations. One question we are particularly excited about is how much these single-neuron mechanisms underlie the remarkable robustness of neural systems. This project will make use of our previously developed machine learning methods and toolbox (e.g., [2-3]), and depending on the selected candidate’s interests, can involve further method development. There will be flexibility to customize the project and ample opportunities to collaborate with top experimental partners locally and internationally.

Your profile

  • You have, or are about to finish, a Master’s degree in a quantitative discipline (e.g., Physics, Computer Science, Mathematics, Machine Learning, Statistics, Bioinformatics, Computational Neuroscience).
  • You have a genuine interest in interdisciplinary work at the interface between neuroscience, modeling, and machine learning.
  • You have good programming skills (ideally in Python).
  • You are a team player with excellent communication skills.
  • Prior experience in deep learning, or/and the statistical analysis of neural data is a plus.

We offer

  • A stimulating, international, interdisciplinary, collaborative, and supportive work environment, which emphasizes diversity and inclusion
  • Scientific guidance at an internationally recognized high-level
  • Access to state-of-the-art compute
  • Competitive salary and benefits
  • Fully-funded position. Although funding is available, the successful candidate will be encouraged and supported in obtaining a personal fellowship (FWO or other)
  • The position is available immediately
  • An environment that promotes personal development

How to apply?

Please complete the application through the VIB online application tool. A complete application file (English) should contain the following documents:

  • your motivation letter stating career goals, past experience, and how these relate to the current position
  • your CV
  • contact information of two references (a minimum of one Professor; one reference can be a Postdoc)

We will review applications on a rolling basis.

For further information, please send an email to with subject title Inquiry: PhD position.

Some relevant publications
1. Deistler M., Macke J.H.*, Gonçalves P.J.* (2022) Energy efficient network activity from disparate circuit parameters. Proc. Natl. Acad. Sci. U.S.A. 119 (44) e2207632119.
2. Gonçalves P.J.*, Lueckmann J.*, Deistler M.*, Nonnenmacher M., Oecal K., Bassetto G., Chintaluri C., Podlaski W.F., Haddad S.A., Vogels T.P., Greenberg D.S., Macke J.H. (2020) Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife, 9, e56261.
3. Lueckmann J.*, Gonçalves P.J.*, Bassetto G., Oecal K., Nonnenmacher M., Macke J.H. (2017) Flexible statistical inference for mechanistic models of neural dynamics. NeurIPS, pages 1289–1299