Baylor College of Medicine-posted about 1 year ago
$75,000 - $75,000/Yr
Full-time • Entry Level
Houston, TX
Educational Services

The Postdoctoral Associate position in Computational Neuroscience and Machine Learning at Baylor College of Medicine involves modeling adaptive computation in biological neural networks and analyzing neural datasets. The role emphasizes collaboration with theorists and experimental labs to develop scientific theories regarding neural circuit computations.

  • Develop theoretical models of neural computation and use these models to guide analysis of neural data.
  • Collaborate with lab members and experimentalists.
  • Read the background experimental and theoretical literature on multi-timescale adaptive computation in neural circuits.
  • Use methods from linear algebra and statistical physics to derive theoretical models of computation in neural circuits.
  • Analyze neural data from experimental collaborators using Python (or an equivalent programming language).
  • Work with other lab members and experimentalist collaborators on a day-to-day basis.
  • Plan, direct, and conduct specialized and advanced research experiments on a day-to-day basis.
  • Summarize research findings and publish results in research journals and conference proceedings.
  • Provide intellectual contribution to other ongoing projects in the lab, and assist PI in shaping the long-term research strategies and directions.
  • Demonstrate commitment to fostering a collaborative lab environment and promoting positive group dynamics.
  • Ph.D. in Chemistry, Computational Sciences, Computational Biology, Structural Biology, Computer Science, Bioinformatics, Statistics, or related disciplines.
  • Ph.D. in Biology or Biomedical Sciences in combination with an M.S. or extensive multidisciplinary experience in one of the above quantitative fields.
  • Ph.D. in a quantitative discipline.
  • Strong foundation in linear algebra.
  • Computer programming experience.
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