Postdoc position in computational neuroscience and machine learning

About the lab

The Gonçalves lab is a recently founded research group at the Neuro-Electronics Flanders (NERF), Belgium, co-affiliated with the VIB Center for AI & Computational Biology . 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 joint research initiative by imec, VIB and KU Leuven.

The project

We are looking for a postdoc candidate interested in using computational methods to study 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 customise the project and ample opportunities to collaborate with top experimental and theoretical partners locally and internationally.

Your profile

• You have a PhD 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.
• You can plan and coordinate your work independently.
• You are not intimidated by writing projects and papers.
• 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.
• An environment that promotes personal development.
• Scientific guidance at an internationally recognized high level.
• Access to state-of-the-art compute.
• Competitive salary and benefits.
• An initial appointment for 3 years with possible extension. 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.

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, experience, and how these relate to the current position.
• your CV.
• contact information of two references.

We will review applications on a rolling basis.

For further information, please send an email to [email protected] with subject title Inquiry: postdoc position.

Some relevant publications

• 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.
• 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.
• 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.