What are the responsibilities and job description for the Postdoctoral Fellowship in Machine Learning and Precision Medicine for Autoimmune Disease position at National Institutes of Health?
The Salivary Disorders Unit at the National Institutes of Health (NIH) is seeking a highly motivated Postdoctoral Fellow with expertise in machine learning (ML), artificial intelligence (AI), and biostatistics/epidemiology to join our multidisciplinary team. This position focuses on developing predictive models of Sjögren’s Disease (SjD) progression using one of the world’s most comprehensive and w longitudinal cohorts of patients with SjD.
About the position
Research Focus: The fellow will play a leading role in building and applying ML/AI methods to integrate longitudinal clinical, molecular, and imaging datasets to identify early predictors of evolving glandular dysfunction, debut of systemic complications, and lymphoma development. Specific computational challenges include:
- Integration of clinical electronic health record and clinical research phenotype data
- Time-series modeling of longitudinal clinical and molecular data.
- Development of deep learning approaches (e.g., LSTMs) to capture temporal dependencies.
- Integration of multimodal datasets (RNA-seq, proteomics, clinical variables, imaging).
- Discovery of predictive biomarkers and risk signatures to guide patient stratification and precision medicine.
Unique Opportunity:
This fellowship offers a rare chance to collaborate closely with a leading pharma/biotech experts and resources. The successful candidate will have access to bespoke computational resources, proprietary AI/ML platforms, and cross-disciplinary innovation networks that go beyond typical academic environments. This collaboration will accelerate the development and validation of predictive models, while providing direct exposure to translational research pipelines that inform drug discovery, biomarker development, and precision medicine strategies.
Responsibilities:
- Design, train, and validate ML/AI models for predicting SjD outcomes.
- Develop pipelines for multimodal data integration and feature selection.
- Collaborate with bioinformaticians, statisticians, and clinical investigators across NIH and industry partners.
- Publish results in high-impact journals and present findings at national and international conferences.
Training Environment:
The NIH Intramural Program offers unparalleled resources for computational discovery, including access to deeply phenotyped longitudinal Sjögren’s cohorts, biobanked samples, and high-dimensional molecular datasets. Fellows will receive structured mentorship in both advanced computational methods and translational applications in autoimmune disease, while engaging in a unique academic–industry collaboration that provides exposure to real-world applications in therapeutic development.
What you'll need to apply
Interested candidates should send a cover letter, CV, and contact information for 3 references to Dr. Blake M. Warner ( blake.warner@nih.gov ) with the subject line: Postdoc Application – ML/AI in Sjögren’s Disease.
Contact name
Blake Warner
Contact email
blake.warner@nih.gov
Qualifications
Qualifications:
- PhD (or equivalent) in computer science, computational biology, bioinformatics, applied mathematics, statistics, or a related discipline.
- Demonstrated expertise in ML/AI methods, particularly applied to biomedical or clinical data.
- Strong programming skills in Python and/or R, including experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
- Experience with time-series or longitudinal data analysis strongly preferred.
- Excellent communication skills and ability to work in a collaborative, cross-disciplinary environment.
Disclaimer/Fine Print
U.S. citizens and permanent residents are eligible to apply.
