Computational Neuroscientist, Modeling / Theory
Tasks
- Analyze fixed points and attractor landscapes
- Build dynamical systems models of brain area interactions
- Build neural activity foundation models
- Contribute to publications and open science
- Derive stimulation control to drive chosen stable states
- Develop NeuroAI architectures
- Develop compositional neural representation models
- Fit dynamical models to noisy partially observed data
- Lead computational research projects end to end
- Mentor research engineers and scientists
- Propose and design experiments to separate competing models
- Stitch cross subject cross species neural data
- Validate stimulation strategies in closed loop experiments
Perks/Benefits
- Collaborative research environment
- Competitive compensation
- Open data
- Open source tooling
- Well resourced lab environment
Skills/Tech-stack
Attractor Models | Bifurcation analysis | Closed Loop | Closed-loop systems | Control Theory | Dynamical systems | Electrical stimulation | Electrophysiology | Fixed point | Fixed point analysis | Foundation Models | GPFA | JAX | Kilosort | LFADS | Latent variable models | Machine Learning | Neural Networks | Neuropixels | Optimal Control | Optogenetics | PyTorch | Python | Real Time | Real time decoding | Recurrent Neural Networks | Robotics | Self-Supervised Learning | Self-supervised | Spike sorting | Stability analysis | State Space Models | State-Space | Statistics | Supervised Learning | Two-photon imaging
Education
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