Computational Neuroscientist, Modeling / Theory
Develop computational and theoretical models of neural representations, dynamics, and control using large-scale recordings across subjects and species. The role combines machine learning, dynamical systems, experimental design, and NeuroAI architecture development, with opportunities to lead research and mentor collaborators.
About the job
Responsibilities
- Build models of compositional neural representation grounded in cognitive theory and fit to recordings.
- Build coupled dynamical-systems models of interactions between multiple brain areas and test them against simultaneous multi-area recordings.
- Analyze fixed-point structures and characterize stable-state landscapes underlying percepts, thoughts, and internal states.
- Derive inputs using optogenetic and electrical stimulation technology to drive systems to chosen stable points, and validate those derivations in closed-loop write-in experiments.
- Stitch data across subjects and species into foundation models of neural activity registered onto a common whole-brain functional and anatomical atlas.
- Propose and help design experiments that distinguish competing models.
- Abstract computational principles from data into new NeuroAI architectures.
- Mentor research engineers and, at senior or principal levels, more junior scientists.
- Contribute to hiring, onboarding, lab culture, publications, talks, open data, tooling releases, and scientific-community engagement.
Requirements
- Strong computational skills, including fluency in Python and modern machine-learning frameworks such as PyTorch or JAX.
- Comfort with large-scale data pipelines and shared computational infrastructure.
- Experience with latent-variable and state-space models of neural population activity, such as GPFA, LFADS, switching state-space models, or recurrent neural network models fitted to data.
- Commitment to open science.
- PhD in computational neuroscience, neuroscience, physics, statistics, applied mathematics, electrical engineering, computer science, control theory, robotics, or a related field, or equivalent research experience.
Nice to Have
- Experience leading computational or theoretical research projects end to end, from formulation through implementation, analysis, and publication.
- Depth in dynamical systems, including fixed-point and attractor analysis, stability and bifurcation structure, and fitting dynamical models to noisy, partially observed data.
- Experience analyzing large-scale neural datasets from electrophysiology, two-photon imaging, or comparable methods, including Neuropixels, Kilosort, and quality-control pipelines.
- Background in control theory, optimal control, or robotics applied to steering high-dimensional systems.
- Background in statistics or theoretical physics applied to neural systems.
- Experience with real-time or closed-loop decoders and model-guided stimulation.
- Experience with foundation-model and self-supervised approaches applied to neural or behavioral data.
- Contributions to open-source neuroscience or scientific-computing projects.
- Ability to work closely with experimental neuroscientists, software engineers, and hardware engineers.
- Experience owning a modeling or theory agenda, making modeling tradeoffs, working across data infrastructure through theory and machine learning, mentoring scientists, or leading technical initiatives.
Compensation and Benefits
- Salary range: $120,000–$220,000, based on location in the Bay Area.
- Competitive compensation package with comprehensive benefits, commensurate with experience.
Skills
Python, PyTorch, JAX, Machine Learning, Large-Scale Data Pipelines, Latent-Variable Models, State-Space Models, Gpfa, Lfads, Dynamical Systems, Neuropixels, Kilosort, Control Theory, Closed-Loop Decoders, Foundation Models
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