Machine Learning Solutions to Chemical Structural Identification from Spectral Sources
| Interdisciplinary Areas: | Data and Engineering Applications, Engineering-Medicine, Human-Machine/Computer Interaction, Human Factors, Human-Centered Design, Others |
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Project Description
The goal of this project is to emulate human deductive reasoning using machine learning models. A critical application of these models will be for structure identification from spectral analytical information sources. Structure identification represents a central bottleneck in many high impact applications, where manual expert deduction remains unavoidable. For these reasons, the development of deductive ML architectures by the postdoctoral candidate will have tremendous methodological and application-specific impact.
Start Date
January 1, 2024
Postdoc Qualifications
A demonstrated track-record of creativity and productivity in either physics-based or machine-learning based chemical modeling.
Co-Advisors
Brett Savoie, bsavoie@purdue.edu, Davidson School of Chemical Engineering, https://engineering.purdue.edu/savoiegroup
Gaurav Chopra, gchopra@purdue.edu, Chemistry, http://www.chopralab.com/index.html