Predoctoral Appointee - Model Validation

Argonne National Laboratory • Lemont, IL

3d • $58,297 - $97,161

About The Position

Join Argonne National Laboratory’s multidisciplinary biomedical data science team to contribute to cutting-edge research at the intersection of artificial intelligence, predictive health modeling, and translational biomedical analytics. This predoctoral appointment offers an opportunity to work on large-scale validation of advanced predictive models derived from harmonized longitudinal human datasets, with applications in long-term health outcome prediction and proactive healthcare decision support. The selected candidate will help evaluate scientific rigor, reproducibility, robustness, and generalizability of computational models in a collaborative environment that integrates data science, biostatistics, and biomedical research.

Requirements

• Completed Master’s degree in computer science, biomedical engineering, applied mathematics, statistics, computational biology, or a related quantitative field.
• Demonstrated experience in programming, with proficiency in Python and familiarity with numerical or scientific computing libraries (e.g., NumPy, PyTorch, TensorFlow).
• Strong aptitude for developing and evaluating machine learning models, including hands-on experience implementing algorithms for classification, regression, or representation learning.
• Ability to analyze complex datasets, design computational experiments, and interpret model performance in a scientifically rigorous manner.
• Excellent written and verbal communication skills, with the ability to work effectively in interdisciplinary research teams.
• Ability to model Argonne's core values of impact, safety, integrity, safety and teamwork.

Nice To Haves

• Experience with healthcare or life sciences data standards such as OMOP, FHIR, CDISC, or related frameworks.
• Familiarity with reproducible ML pipelines, workflow orchestration, or containerized computational environments.
• Experience in survival analysis, longitudinal modeling, or multimodal health data integration.
• Prior work involving model validation, benchmarking, or scientific software testing.

Responsibilities

• Support reproducibility studies of predictive machine learning models by independently executing analytical pipelines on harmonized datasets and verifying reported performance metrics.
• Conduct external validation of predictive models using independent datasets to assess model generalizability across populations and settings.
• Develop and maintain reproducible computational workflows for secure execution of data pipelines and model benchmarking.
• Perform preprocessing, normalization, feature harmonization, and quality control on large longitudinal biomedical datasets.
• Conduct sensitivity analyses to evaluate model robustness under input perturbations, parameter variation, and missing-data scenarios.
• Assess potential bias introduced by data imputation and harmonization methods in long-horizon predictive modeling.
• Generate statistical analyses, benchmarking summaries, visualizations, and technical documentation for internal and external reporting.
• Collaborate with interdisciplinary teams including computational scientists, statisticians, software engineers, and domain researchers to improve model validation methodologies.

Benefits

• comprehensive benefits are part of the total rewards package.
• Click here to view Argonne employee benefits!
• As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation.
• Argonne encourages everyone to apply for employment.
• Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Number of Employees: 1,001-5,000 employees