PhD position in computational neuroscience and machine learning

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.

We are looking for a PhD candidate interested in developing machine learning methods and applying them to neuroscience problems. We are particularly excited about designing new simulation-based inference methods for neuroscience (e.g., [2-3]). However, there will be flexibility to customise the project and ample opportunities to collaborate with top experimental and theoretical partners locally and internationally.

• 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 machine learning and neuroscience.
• 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.

• 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.
• 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.

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 (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 [email protected] with subject title Inquiry: PhD 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.