Outstanding PhD or MD postdoctoral machine learning fellow for a NIH-sponsored two-year T32 fellowship at the Cardiac PET MR CT Program at Massachusetts General Hospital and Harvard Medical School.
The fellow will lead deep learning projects to predict cardiovascular health outcomes (e.g. heart attack) from medical imaging (CT, MRI, PET scans). This will be accomplished in high quality datasets of tens of thousands of patients with imaging and adjudicated outcomes (ROMICAT II Hoffmann U et al. NEJM 2012; PROMISE Douglas PS et al. NEJM 2015; Framingham Heart Study Hoffmann U et al. JAMA Cardiology 2017). The program is well funded and has a track record of academic productivity and grant funding for fellows.
Background in medical imaging or computer vision a plus
Able to work in a collaborative team environment including MDs and PhDs
Excellent communication skills
Interest in manuscript authorship and grant writing
Candidates must have US citizenship or permanent residence. Clinical training on our busy cardiac CT and MRI service is available for MDs who have completed cardiology fellowship or radiology residency. All fellows will be dual-mentored by both clinical and research faculty.
Interested candidates should send a copy of your CV, a personal statement, and three letters of reference to Yuji Liao (email@example.com), Grant Administrator.