Postdoctoral Researcher (Machine Learning for Neural Circuit Modeling)

Postdoctoral Researcher (Machine Learning for Neural Circuit Modeling)

Join us to apply for the Postdoctoral Researcher (Machine Learning for Neural Circuit Modeling) role at Episteme .

Location: San Francisco, CA, USA (On-site). Start date: ASAP. Duration: Initial appointment is for 1 year, with extensions (up to a total of 3 years) contingent upon performance.

We are inviting applications for a Postdoctoral Researcher position in machine learning applied to large‑scale neural activity datasets. This role is central to our program to decode and model C. elegans neural dynamics by integrating experimental recordings with predictive computational frameworks.

Episteme is a new type of R&D company based in San Francisco. We identify, hire, and bring together exceptional scientific researchers across disciplines and geographies—especially individuals who want to pursue difficult and important problems that do not fit into the purview of traditional institutions. Our aim is to create a new path separate from academia, industry, and government for enabling, translating, and commercializing science into tangible impact.

Overview: As a Postdoctoral Researcher, you will lead the development of statistical and machine learning models that predict and explain neural dynamics from experimental recordings. You will work closely with experimentalists to analyze calcium/voltage imaging data, design prospective validation experiments, and develop models that move beyond prediction toward causal understanding. This role is ideal for individuals with strong ML foundations and a deep interest in neuroscience.

Research Focus Areas:

• Predictive modeling of neural activity using high‑dimensional optical recordings
• Statistical inference and causal analysis of circuit‑level dynamics
• Integration of multimodal priors (imaging, behavior, connectomics) into unified models
• Development and application of advanced ML approaches (e.g. graph neural networks, symbolic regression, interpretable models, probabilistic modeling)
• Mathematical formalism to connect learned models with mechanistic circuit hypotheses

Key Responsibilities:

• Develop and test machine learning models for predicting neural activity and behavior from experimental datasets
• Apply statistical and causal inference methods to identify candidate circuit mechanisms
• Collaborate with experimental postdocs to design validation protocols and integrate multimodal data sources
• Explore advanced modeling frameworks (e.g. symbolic regression, GNNs, probabilistic generative models) to enhance interpretability and formal grounding
• Publish results in top‑tier journals and present at major conferences
• Contribute to the collaborative, interdisciplinary environment of the project
• Follow a structured research plan, with defined tasks and milestones

Qualifications:

• Ph.D. in Computer Science, Applied Mathematics, Computational Neuroscience, or a related field
• Strong track record of research in machine learning applied to neural data or other high‑dimensional biological datasets
• Demonstrated expertise in statistical modeling, predictive analysis, and causal inference
• Experience with advanced ML methods such as graph neural networks, probabilistic models, or symbolic regression
• Interest in interpretability and connecting data‑driven results to mechanistic understanding
• Proficiency in scientific programming (Python, PyTorch/JAX, or similar frameworks) and collaborative software development practices
• Strong written and verbal communication skills
• Ability to work both independently and as part of an interdisciplinary team
• Motivation to tackle ambitious, high‑risk/high‑reward problems at the frontier of neuroscience and AI

Application Instructions: To apply, please upload the following materials along with your application in Ashby:

• Curriculum vitae, including a list of publications
• A statement (max 2 pages) describing your research interests, experience with activity imaging, and your most significant scientific contribution to date
• At least two (up to four) letters of recommendation (to be uploaded directly by letter writers to the link provided after application submission)

We strongly encourage early submissions. Positions will remain open until filled. For questions, please contact Dr. Michael Skuhersky at

Additional Information: In addition to competitive salaries, Episteme offers a comprehensive benefits package. You will be part of a collaborative effort bridging neuroscience, machine learning, and advanced imaging, with opportunities for high‑impact publications and career advancement.

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