Postdoctoral Fellow Machine Learning/Data Science

Postdoctoral fellow at Brigham and Women’s Hospital / Harvard Medical School 

(Artificial Intelligence/Machine Learning)

About Our Lab

We are an academic research lab operating with a startup mindset, specializing in developing innovative artificial intelligence and machine learning solutions directly integrated into clinical workflows through Epic EHR systems. Our team is dedicated to significantly improving maternal health outcomes through rigorous translational research, emphasizing creativity, rapid professional growth, and measurable real-world impact.

Role

We are seeking a highly motivated, collaborative individual passionate about developing predictive models that enhance patient safety and prevent adverse pregnancy outcomes. You will utilize multidimensional clinical datasets, including waveform signals (e.g., ECG), genetic data, and imaging, to create predictive algorithms targeting critical maternal outcomes such as hypertensive crises, hemodynamic instability, hemorrhage, and ICU admission. You will also contribute to developing NLP-based and time-series models and integrating these models directly into clinical practice.

Our state-of-the-art data platform provides access to billions of clinical data points from over 300,000 patients, enabling ambitious, publishable work with clear translational pathways. 

You will be part of a multidisciplinary team of data scientists, clinicians, and researchers in a stimulating academic environment, with ample opportunities for collaboration across all Mass General Brigham hospitals, Harvard Medical School, the Program in Medical and Population Genetics at the Broad Institute, and industry partners.

Required qualifications

• PhD (completed or near completion) in a quantitative discipline (computer science, biomedical engineering, biostatistics, data science, bioinformatics, or related). 
• Strong Python and hands-on deep learning experience (PyTorch or TensorFlow). 
• Demonstrated ability to execute rigorous ML research (clean experimental design, evaluation, reproducibility, clear communication). 
• Depth in at least one of the following, with readiness to extend methods into adjacent areas as needed:
  • time-series / waveform ML
  • medical imaging AI (ultrasound experience is a plus)
  • interpretability/error analysis for clinical ML
  • multimodal fusion / clinical deployment-oriented ML