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

About the role

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.

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 interpretability
• Apply statistical and causal inference methods to identify candidate circuit mechanisms
• Develop models and software that can be broadly adopted by the scientific community through open-source or commercial distribution models.
• Contribute to the collaborative, interdisciplinary environment of the project
• Follow a structured research plan, with defined tasks and milestones.

Qualifications

We typically see:

• Ph.D. in Computer Science, Applied Mathematics, Computational Neuroscience, Machine Learning, or a related field
• Strong track record developing machine learning methods for multidimensional signal, image, or sequence data
• Experience building models across predictive, generative, classification, or regression tasks
• Demonstrated expertise in statistical modeling, predictive analysis, and causal inference
• Experience developing impactful computational tools, models, or software systems
• Proficiency with Python and modern machine learning frameworks such as PyTorch or JAX
• Strong scientific communication and collaborative software development practices