Postdoctoral Fellow in Predictive Modeling of Remote Patient Monitoring Data
The Center for Telemedicine and AI?Fortified Precision Digital Health seeks a postdoctoral fellow to develop and validate predictive models that fuse serial multimodal remote patient monitoring signals. The fellow will lead research on transformer?based architectures and advanced biomedical signal processing to predict clinical trajectories from wearables, vital sign monitors, patient?reported outcomes, and voice biomarkers. This is a full?time, research?focused appointment with opportunities for collaboration across clinical, engineering, and data science teams.
Key Responsibilities
Research and Model Development: Design, implement, and evaluate transformer and other sequence models for longitudinal, multimodal patient monitoring data. Signal Processing: Preprocess and extract features from physiological waveforms and sensor streams including ECG, PPG, accelerometry, respiratory signals, and audio. Multimodal Fusion: Develop methods to fuse heterogeneous inputs such as continuous sensor streams, intermittent vital signs, PROs, and voice biomarkers into unified predictive representations. Validation and Deployment: Rigorously validate models using held?out cohorts, cross?site datasets, and clinically meaningful endpoints; collaborate with engineers to prototype reproducible pipelines for deployment and prospective testing. Collaboration and Communication: Work closely with clinicians, device engineers, and regulatory experts; present results at conferences and prepare manuscripts for peer?reviewed journals. Data Governance: Ensure compliance with data security, privacy, and regulatory requirements for protected health information.
Preferred Qualifications
Prior experience with clinical datasets and remote patient monitoring deployments. Familiarity with cloud platforms and scalable data pipelines for streaming or batch processing. Knowledge of regulatory and privacy frameworks relevant to clinical data, including HIPAA. Skills in statistical methods for longitudinal data, survival analysis, and uncertainty quantification. Experience with explainability and fairness techniques for clinical AI models. Software engineering best practices including version control, testing, and containerization. Position Details and Benefits Appointment length: Typical initial appointment is two years with potential renewal based on performance and funding. Mentorship: Direct mentorship from the Center Director and access to multidisciplinary collaborators across clinical and engineering departments. Resources: Access to compute resources, annotated clinical datasets, device integration support, and funding for conference travel. Compensation and benefits: Competitive salary and full institutional benefits consistent with postdoctoral appointments. Application materials required: Curriculum vitae including publication list. Research statement (1-2 pages) outlining past work and proposed projects for this role. Contact information for three references. PhD in Biomedical Engineering, Computer Science, Electrical Engineering, Statistics, Data Science, or a closely related field. Strong experience with transformer architectures and sequence modeling for time series. Proven expertise in biomedical signal processing and analysis of physiological waveforms. Hands?on proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow. Experience with multimodal data including wearables, vital signs, patient?reported outcomes, or audio/voice data. Track record of research productivity demonstrated by first?author publications in relevant peer?reviewed venues. Ability to work independently and collaboratively in interdisciplinary teams.