ML Research Scientist: Collision Cross Section Prediction

Georgia Institute of Technology; Atlanta (GA); On-site; USD 90,000 - 120,000; Full time

Job summary

Georgia Tech in Atlanta invites applications for a Research Scientist I role focused on machine learning for collision cross section prediction in the CCSP project. You will train gradient-boosted and graph-based models, curate large datasets, and contribute to open tools used in metabolomics and lipidomics.

The ideal candidate has strong Python ML experience, familiarity with cheminformatics tools, and effective communication skills for manuscripts and talks.

Qualifications

• Bachelor's Degree required.
• Experience with machine learning in Python, including gradient boosting and graph-based models.
• Familiarity with cheminformatics tools (RDKit, Mordred).
• Knowledge of ion mobility spectrometry and mass spectrometry is a plus.

Responsibilities

• Develop, train, and validate ML models to predict collision cross sections from molecular structure.
• Curate and preprocess large CCS datasets (e.g., METLIN-CCS) and generate molecular descriptors.
• Benchmark studies with multi-seed reproducibility and fair comparisons.
• Diagnose model performance issues and apply regularization and uncertainty quantification.
• Extend framework to additional compound classes and contribute to public releases.

Skills

Python ML

XGBoost

Graph models

Communication

Education

Bachelor's Degree

Tools

RDKit

Mordred

Job description

Georgia Tech in Atlanta invites applications for a Research Scientist I role focused on machine learning for collision cross section prediction in the CCSP project. You will train gradient-boosted and graph-based models, curate large datasets, and contribute to open tools used in metabolomics and lipidomics.

The ideal candidate has strong Python ML experience, familiarity with cheminformatics tools, and effective communication skills for manuscripts and talks.