Postdoctoral Associate (*2 Positions Available*)| Laboratory of Theoretical Neuroscience

Organization Overview

The Zavatone-Veth lab is broadly interested in how neural circuits can implement interesting computations, and how one can identify the computations a neural circuit performs by observing it. Recent advances in machine learning and artificial intelligence open new theoretical possibilities in both directions: On one hand, artificial neural networks can now be trained to perform ever more interesting tasks (including, as of late, proving interesting theorems); we would like to understand how these abilities arise. On the other, these advances provide us with a new set of tools to extract computational insights from measurements of biological neural systems. To use this toolkit effectively, we must understand when it works robustly, and where it can be improved.

About the Role

We are seeking enthusiastic postdocs to join our growing lab as it moves to Rockefeller, starting as soon as January 2027. Successful candidates will have the opportunity to help shape our research in the coming years, and collaborate and mentor other trainees within the group.

Responsibilities

• Designing and executing independent and collaborative research projects
• Communicating research through conference presentations and through preparing manuscripts for publication
• Mentoring trainees, as appropriate

Requirements

• Ph.D. in theoretical or computational neuroscience, physics, applied mathematics, computer science, or a closely related field
• Strong programming (preferably Python) and mathematical skills
• Demonstrated ability to independently design and execute research projects

Preferred Qualifications

• Previous research experience in theoretical neuroscience and/or machine learning
• Previous experience in collaborating with experimental research groups

Pay

Compensation Range: $72,100.00/year. The salary of the finalist selected for this role will be set based on various factors, including but not limited to organizational budgets, qualifications, experience, education, licenses, specialty, and training.