Mentor/s: Meysam HASHEMI <meysam.hashemi@gmail.com/ meysam.hashemi@univ-amu.fr>
Skill level: Intermediate to Advanced
Required skills: Signal analysis, simulations of differential equations, Python and Git; familiarity with neural mass models, JAX, and techniques such as deep neural density estimators would be beneficial.
Time commitment: Full time (350 hours)
About: Virtual Brain Inference (VBI) is a flexible and integrative toolkit for efficient probabilistic inference on virtual brain models. It provides fast simulations of whole-brain models and deep neural density estimators in Python. Extending the scalable implementation to JAX and adding automatic feature extraction will facilitate the use cases.
Aims:
Reproduction of existing use cases,
Changing model parameters and testing,
Integrating new tools to existing workflows,
Use cases in form of notebooks.
The final output will be a lightweight demonstrator with clear documentation, enabling users to quickly run a standardized example. Website: GitHub - ins-amu/vbi: Virtual Brain Inference · GitHub