The Salivary Disorders Unit at the National Institutes of Health (NIH) is seeking to develop predictive models of Sjögren’s Disease (SjD) progression using machine learning (ML) and artificial intelligence (AI) on a comprehensive longitudinal patient cohort.
Requirements
- 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.
Responsibilities
- Design, train, and validate ML/AI models for predicting SjD outcomes.
- Develop pipelines for multimodal data integration and feature selection.
- 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.
Other
- PhD (or equivalent) in computer science, computational biology, bioinformatics, applied mathematics, statistics, or a related discipline.
- Excellent communication skills and ability to work in a collaborative, cross-disciplinary environment.
- U.S. citizens and permanent residents are eligible to apply.
