Research Scientist I - Machine Learning for Collision Cross Section Prediction (

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The School of Chemistry & Biochemistry of The Georgia Institute of Technology

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The School of Chemistry and Biochemistry at the Georgia Institute of Technology invites applications for a Research Scientist I position within the research group of Professor Facundo Fernandez. The successful candidate will advance the CCSP collision cross section prediction platform, with a focus on developing, validating, and extending machine learning models that predict ion mobility collision cross section values directly from molecular structure. The role involves training and benchmarking gradient-boosted and graph-based models on large curated mass spectrometry datasets, including the METLIN-CCS database, and contributing to the public release of predictive tools used across metabolomics and lipidomics. This is a research-intensive position well suited to a candidate with strong computational skills and a working knowledge of analytical mass spectrometry.

Develop, train, and validate machine learning models, including gradient-boosted decision trees and graph-based architectures, to predict collision cross section values from molecular structure.

Curate, clean, and de-duplicate large collision cross section datasets, including the METLIN-CCS database, and generate molecular descriptors using RDKit and Mordred.

Design and execute rigorous benchmarking studies, including multi-seed reproducibility analysis and fair comparison against existing predictors retrained on common data splits.

Diagnose and resolve model performance issues such as overfitting and residual structure, and implement appropriate regularization and uncertainty quantification.

Extend the prediction framework to additional compound classes, including lipids, and contribute to documentation and public release of the platform.

Prepare figures, draft manuscripts, and present results at group meetings and scientific conferences.

Contribute to a collaborative research environment and help mentor graduate and undergraduate students.

Educational Requirements
Bachelors Degree

Demonstrated experience with machine learning in Python, including gradient boosting frameworks such as XGBoost and graph-based model architectures.

Familiarity with cheminformatics tools and molecular descriptor generation, including RDKit and Mordred.

Working knowledge of ion mobility spectrometry, mass spectrometry, and collision cross section measurement.

The ability to communicate results clearly in writing and in oral presentations.

Appllicants should submit:

Cover letter

Curriculum vitae

Names and contact information for three professional references

Inquiries about this position may be directed to:

Facundo M. Fernandez, Ph.D., Regents Professor and Vasser-Woolley Chair in Bioanalytical Chemistry (f24@gatech.edu)

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