Computer Science Research Fellow
About The Position
The Brigham and Women's Hospital is seeking a PhD postdoctoral fellow or PhD candidate with an interest and background in machine learning, particularly medical image processing. The research group is funded by NIH and philanthropic sources to apply deep learning and conventional image-analysis techniques to medical images and text through multimodal learning. Current projects focus on helping radiologists detect and classify vascular abnormalities in brain CT angiography images and reducing interpretation errors in brain CT and MRI through large-language-model summaries of medical-record text. The fellow will work with an interdisciplinary, multi-institutional team of faculty from Harvard Medical School, Mass General Brigham's Brigham and Women's Hospital, Dana-Farber Cancer Institute, and Northeastern University's Khoury College of Computer Sciences. The role involves data acquisition, preprocessing and segmentation, and natural language processing of medical text. The fellow will implement and extend state-of-the-art deep-learning models, typically using Python. Experience with conventional image-processing techniques used in radiology is a plus but is not required. The position is onsite at 15 Francis Street. Applicants should email a CV, cover letter describing research interests and goals, and a full publication list to the hiring manager; applications submitted only through Workday may not be forwarded or reviewed.
Requirements
- Experience with computer vision and/or medical image processing.
- A PhD in computer science or an equivalent discipline is preferred; the posting also welcomes PhD candidates.
- Background and interest in machine learning, particularly medical image processing.
Nice To Haves
- Experience with deep-learning or AI models.
- Experience with conventional image-processing techniques, especially techniques used in radiological imaging.
Responsibilities
- Work with the research team on machine learning, medical image processing, and multimodal modeling.
- Acquire, preprocess, and segment data.
- Apply natural language processing to medical text data.
- Implement and extend state-of-the-art deep-learning models, typically in Python.
- Contribute to projects supporting detection and classification of vascular abnormalities in brain CT angiography images.
- Contribute to projects using large-language-model summaries of medical-record text to help reduce radiology interpretation errors.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
