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

Job ID: 301657 Location: Atlanta, Georgia Full/Part Time: Full-Time Regular/Temporary: Regular About Us Georgia Tech prides itself on its technological resources, collaborations, high-quality student body, and its commitment to building an outstanding community of learning, discovery, and creation. We strongly encourage applicants whose values align with our institutional values. These values include academic excellence, freedom of expression and inquiry, innovation, collaboration, ethical behavior and stewardship. Georgia Tech has policies to promote a healthy work-life balance and is aware that attracting faculty may require meeting the needs of two careers. Georgia Tech is a top-ranked public research university situated in the heart of Atlanta, a vibrant city with numerous economic and cultural strengths. 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For more information on the school, visit chemistry.gatech.edu Job Summary 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. Responsibilities 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. Required Qualifications Bachelors Degree Preferred Qualifications 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. Required Documents to Attach Cover letter Curriculum vitae Names and contact information for three professional references Contact Information Inquiries about this position may be directed to: Facundo M. Fernandez, Ph.D. Regents Professor and Vasser-Woolley Chair in Bioanalytical Chemistry School of Chemistry and Biochemistry & Petit Institute for Bioengineering and Bioscience Georgia Institute of Technology Phone: View phone number on click.appcast.io Email: View email address on click.appcast.io USG Core Values The University System of Georgia is comprised of our 25 institutions of higher education and learning as well as the System Office. Our USG Statement of Core Values are Integrity, Excellence, Accountability, and Respect. These values serve as the foundation for all that we do as an organization, and each USG community member is responsible for demonstrating and upholding these standards. 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