Summary

The Hennig Lab is seeking a postdoctoral researcher passionate about understanding how learning emerges in the brain using computational theory and modeling. In our computational lab you will perform cutting-edge research at the intersection of neuroscience, machine learning, and neural data analysis. Potential research projects are flexible based on the candidate's interest, and include topics such as meta-learning, reinforcement learning, and the emergence of probabilistic representations in the brain. You will also have the opportunity to collaborate on projects with many excellent experimental labs in Baylor College of Medicine and the Houston Medical Center.

This position will provide mentorship through weekly one-on-one meetings with your advisor, and guidance toward a future career in academia or industry.

Candidates within their final year of a Ph.D. are encouraged to apply.

Job Duties

• Plans, directs and conducts research related to theory, modeling, and analysis of behavioral and neural data.
• Evaluates and analyzes neural and behavioral data.
• Summarizes research findings and publish results in research journals.
• Mentors junior lab members on related projects.
• Provides intellectual contribution to other ongoing projects in the lab, and assists PI in shaping the long-term research strategies and directions.
• Demonstrates commitment to fostering a collaborative lab environment and promoting positive group dynamics.

Minimum Qualifications

• Ph.D. in Chemistry, Computational Sciences, Computational Biology, Structural Biology, Computer Science, Bioinformatics, Statistics, or related disciplines. May also include Ph.D. in Biology or Biomedical Sciences in combination with an M.S. or extensive multidisciplinary experience in one of the above quantitative fields.

Preferred Qualifications

• Prior research experience in neuroscience, solid programming skills, and comfort with linear algebra and statistics.
• Experience in analyzing extracellular neural recordings is ideal, but not required.
• Experience with PyTorch or Tensorflow is ideal, but not required.