Undergraduate Research Opportunity (Bowman Group): Machine Learning for Polymer Design 

Can we predict a polymer’s properties before we ever make it?
 
We are seeking an undergraduate researcher for a hands-on project combining polymer chemistry, materials characterization, and machine learning. The student will generate original experimental data by preparing and polymerizing different monomers, then measuring the glass-transition temperatures of the monomers (Tg,m) and resulting polymers (Tg,p). These data will be used to train models that predict polymer properties from molecular structure.
 
The student will gain experience with:
  • Polymer synthesis and sample preparation
  • Thermal characterization techniques such as DSC
  • Experimental design, data analysis, and reproducibility
  • Applying laboratory data to machine-learning research
 
No previous polymer or machine-learning experience is required; training will be provided. We are looking for a curious, dependable student who is excited to conduct hands-on research and take ownership of a project.
 
Commitment: 12 hours per week (3 credits of independent study)
 
Interested students should send a brief statement of interest and résumé or CV to Emma Lochmaier (emma.lochmaier@colorado.edu).  

Posted Aug. 24, 2026