# Computational Neuroscientist, Modeling / Theory

**Company:** [Astera](https://hotfix.jobs/companies/astera)
**Location:** Unspecified
**Role:** AI Research
**Salary:** $120k – $220k/yr
**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
**Posted:** 2026-08-16

> 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.

## Job Description

## 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.

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