Postdoctoral Fellow - Data Engineering & AI/ML

## About the Position The Post-Doctoral Fellow I provides assistance to the Principal Investigator, Co Investigators, and Research Staff. S/he ensures enrollment numbers are successfully met, when applicable, and protocol adherence, monitoring visits, overseeing regulatory and administrative details, data management assistance and project close-out support is provided. ## About the Project This project is a multi-institution research collaboration with Stanford, Johns Hopkins, the Veteran Affairs (VA), and the Department of War (DoW) to build a sleep foundation model. Unlike traditional AI models, a sleep foundation model relies on self-supervised learning on vast amounts of physiological sleep study data (petabyte-level). By generating representations (embeddings) from the sleep data, the team aims to forecast future health risks and uncover latent patterns that traditional sleep scoring metrics miss. ## Qualifications - PhD or MD required - 1-2 years medical research project management experience strongly desired, preferably with Department of Defense - 5+ years experience with multimodal data analysis - Proven experience with signal processing (eg, time-series biosensor data) - Hands-on experience with version control systems (eg, Git) and ability to work in a collaborative coding environment - Experience and/or a strong desire to learn the ins and outs of cloud computing, including managing cloud-native data warehouses and standing up ETL pipelines - Preferred qualifications: strong knowledge of sleep science, familiarity with machine learning workflows including model development, model tuning, and deploying models at scale - Ability to work in a fast-paced, multidisciplinary, multi-site (sometimes asynchronous) team environment - Knowledge of military system, preferred. - Demonstrate competence in oral and written communication - Must be organized, attentive to detail, and possess a positive, friendly and professional demeanor - Ability to be flexible with changing priorities and available to interact with employees, clients and sponsors of all levels - Ability to exercise independent judgment in fast-paced, high-pressured environment with strict deadlines and to manage multiple projects - Computer skills including at least intermediate level experience with Excel and other Microsoft Office Software ## Responsibilities - Design, develop, and deploy scalable applications (both front-end interfaces and back-end data pipelines) to support large-scale research - Build ETL pipelines that ingest multimodal datasets (wearables, cross-referenced with EHRs, PSGs, and subjective sleep metrics), ensuring efficient storage, retrieval, and processing of massive datasets (+1million records) - Support the build-out of scalable infrastructure to support advanced machine learning models using physiological features and sleep microarchitectures - Optimize application performance and scalability through performance tuning, code refactoring, and database optimization techniques - Stay updated with emerging industry trends, academic literature, and technologies in data engineering, cloud architecture, and machine learning to continuously improve the lab's technical capabilities - Ensure data integrity throughout engineering workflows. Assist in the preparation of Standard Operating Procedures (SOPs), analytical frameworks, and technical documentation - Maintain open communication with leadership, advise on technical processes, and curate progress reports - Provide technical support and oversight to team members with less experience - Assist in regulatory support, Data Sharing Agreements, and other project documentation - Collaborate and assist the Principal Investigator, Co-Investigators and research team in the performance the research study - Contribute to the drafting and submitting of abstracts, manuscripts and reports for dissemination - Prepare progress reports on research as required - Analyze and write-up data - Present complex information effectively to a range of audiences - Provide data management assistance