Develop methods and tools to effectively use electronic health record (EHR) data; apply AI/ML methods to solve critical clinical and biomedical problems.

Implement methods into software that meets research needs, manage and update source codes as needed. Work in an interdisciplinary team of informaticists, programmers, information quality experts, statisticians, and researchers during software development.

Lead and participate in the design, implementation, and reporting of research and evaluation studies. Contributions to scientific reports, conference papers, journal articles, and grant proposals are expected, including documentation of method, presentation of data, and participation in interpretation of results.

Lead and participate in collaborative projects and contribute to data processing, software development, and other collaborative efforts.

A Ph.D. in biomedical informatics, computer science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM fields.

Strong programming and data analysis skills (e.g., Python, R)

Solid understanding of machine learning, deep learning, and data modeling techniques

Prior working experience with EHR data, machine learning, deep learning, imaging informatics, and large language models (LLM) is preferred.

Prior working experience with popular ML packages, e.g., PyTorch, Scikit-learn, TensorFlow, Pandas, Keras, NumPy, SciPy, etc., and traditional ML models such as support vector machines (SVMs) and Gradient Boosting Machines (GBM), as well as deep learning models (GNN, CNN, GCN, etc.) including LLMs.

Being able to develop informatics tools and software packages.

Excellent formal and interpersonal communication skills and the ability to communicate effectively to a diverse audience, such as students, clinicians, and PIs.