Are you an upper-level undergraduate student, graduate student, scientist, or faculty member interested in learning how to integrate computational models of living systems into your research or courses but have little prior experience? If so, Professor Joshua Weitz and Professor James C. Gumbart along with the Quantitative Biosciences (QBioS) first-year cohorts encourage you to apply to our 2023 in-person two-day workshop on machine learning for studying viral molecular dynamics! It will feature:
- An opening lecture on the core principles behind stochastic gene expression by Dr. Anna Pavlova, Georgia Tech.
- Hands-on tutorials on implementing machine learning algorithms to understand viral molecular dynamics using
Python and visualization using 
PyMOL. - Plenary lecture by Professor Juan Perilla, University of Delaware (UD).
- Lunch and coffee breaks
We welcome applications from all irrespective of prior coding experience. The workshop will include an introductory programming session (Python and PyMOL) for those with limited/no coding experience. All participants will learn the basics of developing machine learning algorithms for understanding viral molecular dynamics using random forests and linear regression as well as how to apply the algorithm using real data. We will provide same-day support for coding in Python and PyMOL.
We thank the Burroughs Wellcome Fund and NSF TRIPODS+X:EDU for making this workshop possible.
The Workshop is concluded. Follow @QBioS_GT on twitter to stay updated for the 2024 workshop
For any queries mail to qbiosworkshop@gatech.edu