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.

We are looking for a PhD candidate interested in using computational methods to study the role of different scales — from membrane and synaptic biophysics to connectivity patterns — in conferring robustness to 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.

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.