Research Fellow Artificial IntelligenceMachine Learning
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Job Location:
Boston, NH - USA
Monthly Salary:
Not Disclosed
Posted on:
Yesterday
Vacancies:
1 Vacancy
Job Summary
Mass General Brigham relies on a wide range of professionals including doctors nurses business people tech experts researchers and systems analysts to advance our mission. As a not-for-profit we support patient care research teaching and community service striving to provide exceptional care. We believe that high-performing teams drive groundbreaking medical discoveries and invite all applicants to join us and experience what it means to be part of Mass General Brigham.
Job Summary
About Our LabWe 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.
Qualifications
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 300000 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:
o time-series / waveform ML
o medical imaging AI (ultrasound experience is a plus)
o interpretability / error analysis for clinical ML
o multimodal fusion / clinical deployment-oriented ML
How to apply
Email with subject Application for AI/ML postdoc position and include: CV cover letter (research background interests) and 3 references.
Additional Job Details (if applicable)
Remote Type
Work Location
EEO Statement:
Mass General Brigham Competency Framework
At Mass General Brigham our competency framework defines what effective leadership looks like by specifying which behaviors are most critical for successful performance at each job level. The framework is comprised of ten competencies (half People-Focused half Performance-Focused) and are defined by observable and measurable skills and behaviors that contribute to workplace effectiveness and career success. These competencies are used to evaluate performance make hiring decisions identify development needs mobilize employees across our system and establish a strong talent pipeline.
Key Skills
- Bioinformatics
- Genetics
- R
- Biochemistry
- Cell Biology
- Research Experience
- Statistical Software
- Cell Culture
- Molecular Biology
- Data Analysis Skills
- Flow Cytometry
- Microscopy
About Company
Patients at Mass General have access to a vast network of physicians, nearly all of whom are Harvard Medical School faculty and many of whom are leaders within their fields.
