Job description

Company Overview Astera Neuro seeks to understand and engineer consciousness. We believe consciousness is the process by which the brain builds a coherent internal model of the world and the self. We aim to understand this model and to develop the technologies to write experience directly into the brain. By combining large-scale neural recording, causal perturbation, and brain-based AI, we seek to move neuroscience from observing correlates of experience to engineering it. The same tools carry direct therapeutic promise for neurological and psychiatric diseases that alter perception, thought, and sense of self. Because the science and tools required do not yet exist, we are assembling a founding team of neuroscientists, theorists, and engineers to create them. We do high-risk, high-reward science in a well-resourced, collaborative environment with competitive pay, and share everything openly under Astera's Open Science Policy. Position Summary Research Scientists in Theory & Computation 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. What You’ll Do - 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. Who You Are Required: - 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. Preferred/Nice to Have: - Experience with latent-variable and state-space models of neural population activity, such as GPFA, LFADS, switching state-space models, or recurrent network models fit to data. - Background in control theory, optimal control, or robotics, particularly as applied to steering high-dimensional systems toward target states. - Background in statistics or theoretical physics, with a serious interest in applying it to neural systems. - Experience designing or implementing real-time or closed-loop decoders and model-guided stimulation. - Experience with foundation-model and self-supervised approaches applied to neural or behavioral data, and interest in the correspondence between artificial and biological representations. - Contributions to open-source neuroscience or scientific computing projects. What We Value - Conviction that the brain’s internal model can be understood in full, and recognition that getting there requires a kind of science no single academic lab can do. - Appetite for building theory rather than applying it: the framework that explains how a physical system converges on a stable internal model of the world doesn’t yet exist, and we’re looking for people who want to build it. - Willingness to be held to an engineering standard: our decisive tests are write-in experiments, not merely decoding. What we cannot build, we do not understand. - Comfort building on shared infrastructure rather than private projects. - Commitment to open science. Additionally Expected at Senior / Principal Level - Track record of owning a modeling or theory agenda end to end, including framing the question, collaborating with experimentalists, and delivering results. - Experience making and defending modeling tradeoffs across interpretability, predictive accuracy, and experimental utility. - Comfort working across the stack, from data infrastructure and analysis pipelines through to theory and machine learning. - History of mentoring scientists or leading technical initiatives (Principal level). Why Join Us Research Scientists in Theory & Computation at Astera Neuro, focused on answering the hardest and least understood questions in neuroscience: how the brain generates thoughts, intelligence, and consciousness. We seek to understand the compositional structure of neural representations and the dynamics that give rise to conscious access, and to build the theory that explains how a physical system converges on a stable internal model of the world. Astera Neuro provides a comprehensive benefits package and total compensation that is competitive and commensurate with the level of experience and qualifications. We are an equal opportunity employer and value diversity and inclusion!

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Company Overview
What You’ll Do
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.
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.
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 state-space models of neural population activity, such as GPFA, LFADS, switching state-space models, or recurrent network models fit to data.
Background in control theory, optimal control, or robotics, particularly as applied to steering high-dimensional systems toward target states.
Background in statistics or theoretical physics, with a serious interest in applying it to neural systems.
Experience designing or implementing real-time or closed-loop decoders and model-guided stimulation.
Experience with foundation-model and self-supervised approaches applied to neural or behavioral data, and interest in the correspondence between artificial and biological representations.
Contributions to open-source neuroscience or scientific computing projects.
What We Value
Conviction that the brain’s internal model can be understood in full, and recognition that getting there requires a kind of science no single academic lab can do.
Willingness to be held to an engineering standard: our decisive tests are write-in experiments, not merely decoding. What we cannot build, we do not understand.

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aimachine learningpython

Platform: ashby
Effort: quick

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