Important Info
For inquiries and submitting the required material please contact: anooprao@stanford.edu with the subject Application for a Postdoctoral Fellowship
The Department of Pediatrics, Division of Neonatology and the Neonatal Engineering, Signals, and Technology (NEST) Lab are seeking a creative, motivated, and collaborative Postdoctoral Fellow to join our team and conduct research focused on physiologic waveform analysis, biomedical signal processing, and computational modeling of continuous clinical monitoring data.
The successful candidate will work on projects involving the analysis of physiologic waveforms collected from neonatal, pediatric, and/or adult patients in clinical care settings. These data may include arterial blood pressure waveforms, pulse oximetry, photoplethysmography, ECG, respiratory waveforms, heart rate, blood pressure trends, and other bedside monitoring signals. The goal of this work is to develop, validate, and translate computational methods that extract clinically meaningful information from continuous physiologic signals and support improved monitoring, risk detection, and decision-making at the bedside.
The research program lies at the intersection of biomedical engineering and clinical physiology. Projects may involve signal quality assessment, artifact detection, waveform segmentation, feature extraction, hemodynamic modeling, time-series analysis, machine learning, and development of algorithms for real-world clinical data. The fellow will have the opportunity to work with clinicians, engineers, data scientists, trainees, and research staff in a highly collaborative environment.
The postdoctoral fellow will drive an independent research project while also contributing to team-based projects involving physiologic waveform datasets and clinical outcomes. The fellow will work closely with the principal investigator, Dr. Rao, on project development, algorithm design, data analysis, interpretation of physiologic signals, manuscript preparation, and grant or fellowship applications. This position is ideal for a candidate interested in applying quantitative methods to clinically important problems and translating computational tools into improved patient care.
There will be opportunities to collaborate with a variety of scientists and clinicians at Stanford University as well as other institutions.
Example Project Areas
The fellow may contribute to one or more of the following areas:
- Development of algorithms to analyze continuous physiologic waveforms from bedside monitors.
- Detection and correction of motion artifact, signal dropout, and poor-quality waveform segments.
- Extraction of clinically meaningful features from blood pressure, pulse oximetry, ECG, respiratory, or photoplethysmography signals.
- Modeling of cardiovascular or respiratory physiology using continuous monitoring data.
- Development of machine learning models to identify physiologic instability or changes in patient status.
- Validation of waveform-derived metrics against clinical reference standards.
- Integration of physiologic waveform features with clinical metadata and outcomes.
- Creation of reproducible Python and/or MATLAB pipelines for clinical signal processing and analysis.
- Translation of computational methods into clinically interpretable tools for bedside monitoring.
- PhD, MD, MD-PhD, or equivalent degree in biomedical engineering, electrical engineering, computer science, data science, applied mathematics, physiology, biostatistics, or a related field.
- Strong programming skills in Python and/or MATLAB.
- Experience analyzing physiologic signals, biomedical waveforms, time-series data, or other high-dimensional biomedical datasets.
- Strong foundation in signal processing, statistical analysis, computational modeling, or algorithm development.
- Ability to process, clean, analyze, and interpret complex datasets.
- Comprehensive understanding of scientific principles and rigorous experimental or analytical design.
- Strong general computer skills and ability to quickly learn new software tools, databases, and analytical workflows.
- Excellent organizational, communication, and problem-solving skills.
- Demonstrated excellence, innovation, and productivity in research.
- Ability to drive projects both as a project lead and as a collaborative team member.
- Ability to develop and write manuscripts and contribute to independent grant, fellowship, or project funding applications.
Desired Qualifications:
- Hands-on experience with cardiovascular, respiratory, hemodynamic, ECG, arterial blood pressure, pulse oximetry, photoplethysmography, or other physiologic waveform data.
- Experience with filtering, denoising, waveform segmentation, feature extraction, signal quality assessment, artifact detection, or time-series modeling.
- Experience with Python scientific computing tools such as NumPy, SciPy, pandas, scikit-learn, PyTorch, TensorFlow, matplotlib, or related packages.
- Experience with MATLAB-based signal processing workflows.
- Familiarity with clinical data sources, electronic health record data, ICU/NICU monitoring systems, bedside monitoring devices, or medical device data.
- Experience linking physiologic waveform features to clinical outcomes.
- Experience with machine learning, deep learning, predictive modeling, or real-time data analysis.
- Familiarity with data visualization, reproducible research workflows, version control, and collaborative coding practices.
- Interest in translating computational methods into clinically useful tools for neonatal, pediatric, or critical care medicine.
- Ability to work independently and function effectively as part of a multidisciplinary team of clinicians, engineers, scientists, students, postdoctoral fellows, and research staff.
- Letter of interest and summary of previous research experience, approximately one page
- Current CV or resume
- Contact information for three references