Postdoc in Computational Neuroscience and Machine

il y a 2 semaines


Leuven, Belgique VIB Temps plein

Posted Date
- 04 Mar 2024
- Locations
- Leuven
- Center
- Neuro-Electronics Research Flanders (NERF)
- Lab
- Pedro Gonçalves Lab
- Type
- Post-doctoral
- Positions
- 1

**About the lab**:
NERF is a joint research initiative by imec, VIB and KU Leuven.

**The project**:
**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.
- The position is available immediately.

**How to apply?**:

- your motivation letter stating career goals, experience, and how these relate to the current position.
- your CV.
- contact information of two references.

**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.



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