About Episteme Episteme is a new kind of R&D company built for people who want their work to matter in the world. We support exceptional researchers pursuing ambitious, translational science; work that often struggles to find the right home within traditional academic or corporate institutions. At Episteme, we provide the financial, infrastructural and operational support that allows researchers to focus on the problems that genuinely deserve their attention.

We work closely with researchers to move ideas from early insight to real-world application. Translation is part of the work, not an afterthought. When breakthroughs are ready, we help bring them into the world responsibly, including through commercialization when appropriate.

Working at Episteme means joining a company that is intentionally different. We are building something new, and that requires comfort with ambiguity, intellectual honesty, and a bias toward thoughtful execution. You will work alongside people from many disciplines who care deeply about rigor, impact and follow-through.

We value clarity over complexity, ownership over passivity and collaboration without micromanagement. Roles evolve through contribution and trust rather than rigid job descriptions, and ideas are tested quickly, decisions are made explicitly, and learning is shared openly so the system improves faster than any individual. What We Look For This is a Member of Research Staff role, equivalent in scope and career stage to a postdoctoral researcher position.

The position is about developing statistical and machine learning models that process, explain, and predict neural dynamics from large-scale experimental recordings. You will work within the research group of Dr. Michael Skuhersky, contributing to a broader effort to decode and model neural dynamics in C.

elegans by combining experimental observations with computational models. Working closely with both computational and experimental scientists, you will develop and deploy machine learning models for imaging data, neural activity traces, and predictive and causal modeling of neural circuits. You have strong foundations in machine learning and statistical modeling and are excited by the challenge of applying those tools to difficult scientific questions.

You understand that biological systems are noisy, incomplete and often resistant to simple explanations. Rather than treating this complexity as a limitation, you see it as an opportunity to build better models and ask better questions. You operate with a high degree of independence within a defined research scope.

You can take broad objectives and translate them into experiments, analyses, and models while exercising sound scientific judgment. You know when to push forward independently and when to seek input from collaborators. You are motivated by learning, discovery, and scientific rigor.

You care not only whether a model performs well, but also whether it improves understanding, generates useful hypotheses, and helps move the field forward.

Key Responsibilities

Develop and test ML models for processing and integrating multi-modal data priors (e.g., calcium imaging, connectomics, and voltage dynamics of individual neurons and circuits) Develop and test ML models for predicting neural activity and behavior from experimental datasets Explore advanced deep learning methods (e.g., symbolic regression, GNNs, Transformers, generative models) to inform neural models from data and enhance int

Member of Research Staff (Machine Learning for Neural Circuit Modeling Postdoc)