Research Scientist - Computational Neuroscience

About the role

Astera Institute is seeking a Research Scientist in Computational Neuroscience to develop computational models of neural circuits and brain function. You will read, analyze, and synthesize neuroscience, cognitive science, and computational neuroscience literature to identify gaps and opportunities for novel theoretical work. You will develop biologically plausible computational models of neural circuits, brain regions, and large-scale brain systems that can explain how computation emerges from biological substrates. You will compare model predictions with experimental data and refine models accordingly, using an iterative cycle of simulation and validation. You will develop novel algorithms inspired by biological neural computation, translating insights from biology into efficient computational procedures. You will analyze large-scale neural and behavioral datasets to inform and evaluate computational models, ensuring that theories remain grounded in empirical reality. You will stay current with advances in neuroscience, machine learning, and computational modeling to maintain a cutting edge research program. You will propose and pursue original research directions that advance our understanding of brain computation and contribute to the broader scientific conversation.

Key facts

What you'll do

• Synthesize interdisciplinary literature spanning neuroscience, cognitive science, and computational neuroscience to frame new research questions.
• Construct biologically plausible computational models of neural circuits, brain regions, and large-scale brain systems using modern simulation frameworks.
• Validate model outputs against experimental neural and behavioral data, refining model architectures and parameters to improve biological fidelity.
• Design and implement novel algorithms inspired by biological neural computation, emphasizing efficiency, robustness, and biological realism.
• Process, clean, and analyze large-scale neural recordings and behavioral datasets to extract features that inform model development.
• Monitor and integrate emerging techniques from neuroscience, machine learning, and computational modeling to maintain a state-of-the-art research agenda.
• Drive original research initiatives that uncover fundamental principles of brain computation and communicate insights through rigorous theory building.
• Generate new theoretical contributions to computational neuroscience by developing models that explain neural dynamics, coding, and computation.
• Document research findings in detailed technical reports, peer-reviewed publications, and clear presentations for both technical and interdisciplinary audiences.
• Collaborate with experimentalists and engineers to align models with empirical constraints and to translate theoretical findings into practical applications.
• Maintain a flexible and evolving role that adapts to new scientific questions and emerging opportunities within the institute over a 6-12 month horizon.
• Operate with bias to action, launching simulations and analyses quickly while iterating on models based on empirical feedback.
• Leverage AI-driven tools and modern computational infrastructure to amplify research productivity and model complexity.
• Uphold the highest standards of scientific integrity, validating theories with data before treating them as operational principles.

Requirements

• Experience programming in Python is essential for implementing models, analysis pipelines, and simulations.
• Familiarity with scientific computing libraries such as Jax, Pytorch, NumPy, SciPy, or similar tools is required for efficient numerical computation.
• Research experience in systems, computational, and theoretical neuroscience is necessary to understand biological constraints and formulate testable models.
• Experience working with data analysis, statistics, or machine learning workflows is required to process empirical datasets and evaluate model performance.
• Ability to work independently and communicate technical findings clearly to diverse audiences is required for driving research initiatives and disseminating results.
• Demonstrated capability to learn and apply new frameworks, programming languages, and high-performance computing tools as needed for complex modeling tasks.
• Strong quantitative background and comfort with mathematical reasoning, including dynamical systems, probability, and optimization concepts.
• Commitment to following the scientific method, treating theories as hypotheses to be tested and validated against empirical observations.

Nice to have

• Experience with machine learning or dynamical systems modeling is preferred for building and analyzing complex neural systems.
• C++ programming experience is a strong bonus for performance-critical components and integration with existing systems.
• Familiarity with Linux, Git, and high-performance computing environments is preferred to streamline development and collaboration workflows.
• Interest in wet lab experiments to collect more data or test theories is encouraged to close the loop between experimentation and modeling.
• Neural circuit modeling expertise is valued for constructing detailed models of microcircuits and network dynamics.
• Synaptic plasticity and learning mechanisms are preferred topics for understanding how experience shapes circuit function.
• Large-scale brain simulation experience is a strong asset for bridging models across scales from neurons to systems.
• Data-driven modeling of neural systems is preferred for integrating heterogeneous datasets and discovering latent structure.

Practical notes

Some travel may occasionally be required for collaboration and team events.