Job Description
- Developing new statistical methods to model multimodal neuroimaging data, multi-omics data, electronic health record (EHR) and genetics data which are measured by longitudinally and cross-sectionally.
- Developing and applying machine learning and AI approaches to identify interactive topological relationships between various imaging modalities and multi-omics during aging and development.
- Implementing computationally intensive algorithms on high-performance computational clusters.
- Developing AI/ML-based causal inference models for real-world evidence applications (e.g., target trial emulations- TTE)
- Developing data integration methods to combine multiple massive datasets (UKBB, HCP, ENIGMA, and etc) and local trials and observational studies (e.g, UMMS EHR).
- Manuscript drafting and preparation.
- Performing other duties as assigned
Responsibilities
Post Doctoral Fellow.
Qualifications
Doctorate or recognized terminal degree in the field of specialization. Demonstrated research abilities. May require unrestricted license in the State of Maryland.
