Computational Neuroscientist, Modeling ​/ Theory

Company Overview

Astera Neuro, part of the Astera Institute, is building the tools to decipher and ultimately write the neural codes behind perception, thought, behavior, and internal state. Since these tools don't yet exist, we're assembling a founding team of neuroscientists, computational scientists, and engineers to build them (hardware, software, and methods) and use them to study neural activity at unprecedented scale, with direct relevance to neurological and psychiatric disease.

We do high-risk, high-reward science in a well-resourced, collaborative environment with competitive pay, and share our work openly under Astera's Open Science Policy.

Computational Neuroscientists at Astera Neuro develop and lead research programs aimed at understanding the representations and dynamics underlying conscious access, and at converting that understanding into the ability to steer the system. The work sits at the intersection of cognitive theory, large-scale neural data, and machine learning, and draws on recordings from our primate, rodent, and human programs. The opportunity here is to create an entirely new theoretical framework for how the brain builds an internal model of the world, on data of a scale, breadth, and quality that has not previously existed.

Computation sits at the vital core of Astera Neuro. The role is built around tight coupling with experiment; models are expected to make testable predictions, and to propose the next experiment rather than wait for it. Title and scope are calibrated to track record.

Successful applicants will focus on one or more of Astera Neuro’s primary research tracks, with background and interest helping determine the best fit. Some positions suit scientists with a bent for modeling large-scale neural data, extracting structure from recordings that span many areas, sessions, animals, and species. Others suit scientists with a more theoretical bent, concerned with abstracting the key computational principles out of the data and into new NeuroAI architectures.

We welcome computational scientists trained outside neuroscience; backgrounds in control theory, robotics, theoretical physics, statistics, and machine learning are all highly valued here.

• Build models of compositional neural representation, grounded in cognitive theory and fit to recordings: how the brain binds content to variables, composes structured thought, and updates it.

• Build coupled dynamical-systems models of the interactions between multiple brain areas, and test them against simultaneous multi-area recordings.

• Analyze the fixed-point structure of these systems and characterize the landscape of stable states underlying percepts, thoughts, and internal states.

• Derive how to sculpt inputs, using our optogenetic and electrical stimulation technology, to drive the system to chosen stable points, and validate those derivations in closed-loop write-in experiments.

• Stitch data together across subjects and species into foundation models of neural activity, registered onto a common whole-brain functional and anatomical atlas.

• Propose and help design new experiments: identify the measurement or perturbation that would most sharply separate competing models, and work with the experimental teams to run it.

• Abstract key computational principles from the data into new NeuroAI architectures, in some cases in direct collaboration with Astera AI.

• Mentor research engineers and, at the senior or principal level, more junior scientists; contribute to hiring, onboarding, and lab culture.

• Contribute to publications, talks, open data and tooling releases, and engagement with the broader scientific community.

• PhD with 3-12+ years of experience in computational neuroscience, neuroscience, physics, statistics, applied mathematics, electrical engineering, computer science, control theory, robotics, or a related field, or equivalent research experience. Graduate work or research experience in neuroscience is a plus but not required.

• Demonstrated ability to lead a computational or theoretical research project end to end, from question and formulation through implementation, analysis, and publication.

• Depth in dynamical systems, including fixed-point and attractor analysis, stability and bifurcation structure, and the fitting of dynamical models to noisy, partially observed data.

• Strong computational skills, with fluency in Python and modern machine learning frameworks such as PyTorch or JAX, and comfort with large-scale data pipelines.

• Hands-on experience analyzing large-scale neural datasets from electrophysiology, two-photon imaging, or comparable methods, including high-density recordings such as Neuropixels and modern spike-sorting and quality-control pipelines such as Kilosort.

• Ability to work at close quarters with experimental neuroscientists, software engineers, and hardware engineers in a fast-moving, multi-team environment.

• Experience with latent-variable and…