Summary:
Episteme is seeking a highly skilled researcher in machine learning to support the translation of sophisticated science into real-world applications. This postdoc-equivalent position involves collaborating on groundbreaking research to decode neural dynamics in C. elegans using advanced statistical and machine learning techniques.
Research Staff Member in Machine Learning for Neural Circuit Modeling, Episteme, California
Designation:
Member of Research Staff
Location:
San Francisco, California
Research Area:
- Predictive modeling of neural activity from optical recordings
- Integration of multimodal data including imaging, behavior, and connectomics
- Statistical inference and causal analysis of circuit-level dynamics
- Development of cutting-edge machine learning approaches (e.g., graph neural networks, symbolic regression)
| Key Responsibilities | Success Metrics |
|---|---|
| Develop ML models for multi-modal data | Models enhance understanding and scientific insight |
| Test predictive models for neural activity | Effective models lead researchers in hypothesis generation |
| Apply advanced deep learning methods | Positive contributions to collaborative research environment |
| Create broadly adoptable tools and software | Consistent delivery of high-quality research |
| Follow structured research plans | Progress is evident through defined milestones |
Eligibility/Qualification:
- Ph.D. in Computer Science, Applied Mathematics, Computational Neuroscience, Machine Learning, or related field
- Strong track record in developing machine learning methods for multidimensional data
- Proficiency in Python and modern ML frameworks (e.g., PyTorch, JAX)
- Experience in statistical modeling, predictive analysis, and causal inference
Job Description:
The selected candidate will develop machine learning models for analyzing data from large-scale neural recordings. Responsibilities will include implementing predictive models, collaborating with both computational and experimental scientists, and applying advanced statistical methods to inform hypotheses about neural circuit functions. The role requires a balance of independence in research while valuing collaborative contributions.
How to Apply:
Interested candidates should submit their resume/CV, a cover letter, and any relevant supporting documents. Applications can be submitted directly through the Episteme careers page.
Last Date to Apply:
Open Now
Applications are accepted on a rolling basis; candidates are encouraged to apply as soon as possible.
