Summary

The laboratory of Dr. David Lipshutz in the Department of Neuroscience at Baylor College of Medicine, Houston, Texas is seeking applications for a Postdoctoral Associate.

The candidate will model adaptive computation in biological neural networks and leverage theoretical insights to analyze neural datasets. They will collaborate with theorists and experimental labs at Baylor College of Medicine, Rice University and the Houston Medical Center to develop scientific theories about the adaptive computations performed in neural circuits.

Job Duties

• Develops theoretical models of neural computation and use these models to guide analysis of neural data.
• Collaborates with lab members and experimentalists.
• Reads the background experimental and theoretical literature on multi-timescale adaptive computation in neural circuits.
• Uses methods from linear algebra and statistics physics to derive theoretical models of computation in neural circuits.
• Analyzes neural data from experimental collaborators using Python (or an equivalent programming language).
• Works with other lab members and experimentalist collaborators on a day-to-day basis.Plans, directs and conducts specialized and advanced research experiments on a day to day basis.
• Summarizes research findings and publish results in research journals and conference proceedings.
• Provides intellectual contribution to other ongoing projects in the lab, and assist 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

• Ph.D. in a quantitative discipline and Strong foundation in linear algebra and computer programming experience is preferred.