We are always looking for talented individuals who are passionate about using computational and statistical approaches to improve our understanding of human health and disease. Please do not hesitate to contact us. Below are examples of fellowships that may support trainees, followed by previous opportunities in the lab.
The TOPMed Fellowship supports early-career researchers applying AI and machine learning approaches to data-driven questions in heart, lung, blood, and sleep research, with opportunities to work with TOPMed’s rich trans-omics resources.
The American Heart Association offers one- or two-year fellowships for postdoctoral researchers working in cardiovascular and brain health research.
BAEF fellowships support eligible Belgian researchers—including master’s-level, doctoral, and postdoctoral trainees—pursuing research in the United States.
This joint role focused on aging, somatic mosaicism, multimodal phenotyping, machine learning, and statistical genetics using large-scale biomedical datasets.
Candidates were expected to hold a master’s degree in a quantitative field, have strong R or Python skills, and bring excellent organizational, communication, and collaborative abilities.
This role invited quantitative scientists to lead projects in aging and somatic mosaicism, multimodal phenotyping and machine learning, or omics and statistical methods.
The position emphasized committed career development, flexible co-mentorship, access to rich multi-cohort data, opportunities to develop mentorship and leadership skills, and support for fellowship and grant applications.
This joint opportunity with Dr. Michael Honigberg focused on polygenic risk scores, rare variants, genetic association studies, and classical epidemiology.
The role was designed for post-bachelor’s or post-master’s researchers seeking hands-on experience, scientific writing and presentation opportunities, and preparation for future doctoral training.
Computational modeling of multimodal data in aging, somatic mosaicism, and cardiovascular disease.
100 Cambridge Street, Office 1576, Boston, MA 02114
75 Ames Street, Cambridge, MA 02142
zyu5@mgh.harvard.edu
zyu@broadinstitute.org