Computational Associate

Description: The Yu and Small Labs at Mass General Brigham and the Broad Institute of MIT and Harvard are seeking one motivated computational associate to join our teams!

The Yu Lab develops and applies computational and statistical methods for multi-omics data (germline and somatic DNA, methylation, RNA, and protein) and other multi-modal data (medical imaging, EHR) to investigate mechanisms and potential interventions for cardiovascular and other age-related diseases. We are especially interested in and funded to study age-related somatic mutations, in blood (clonal hematopoiesis) and other tissues, and their disease consequences.

The Small Lab focuses on translational approaches to valvular heart diseases, including calcific aortic stenosis, mitral valve prolapse, and rheumatic heart disease. The lab employs state-of-the-art statistical methods to multi-omics data, including genomics, transcriptomics (bulk tissue, single cell, and spatial), and proteomics data to interrogate biologic mechanisms with an ultimate goal to identify novel treatment targets. The lab is also interested in developing novel imaging-derived phenotypes for biologic discovery and clinical risk prediction.

Responsibilities: The candidate will contribute to projects in one or more of these areas:

• Somatic Mutations & Age-related Disease: How age-related somatic mutations, particularly clonal hematopoiesis, contribute to disease. The candidate will likely be involved in somatic mutation calling from whole genome/exome sequencing data.
• Machine Learning & Multi-modal Data: Applying machine learning to imaging (e.g., echocardiography, CT) and clinical notes, and integrating these with omics data for biological discovery and risk prediction.
• Statistical Genetics: Analyzing common variant genotyping and whole genome/exome sequencing data from Mass General Brigham, UK Biobank, All of Us, and other resources for genetic discovery and risk prediction.

Qualifications:

• Master's degree in a quantitative field such as Biostatistics, Bioinformatics, Computational Biology, Electrical or Biomedical Engineering, or Genetic Epidemiology.
• Strong programming skills (e.g., R or Python).
• Experience analyzing large-scale, complex datasets preferred.
• Excellent organizational, communication, and collaborative skills.

What We Offer: This role is ideal for post-master’s students seeking research experience and publications before applying to PhD programs.

• Career Development: Active support to build your publication record and PhD application profile. We have a very strong track record with master's-level trainees - recent alumni have gone on to PhD programs at Harvard , Cambridge, etc.
• Rich, Large-Scale Data: Extensive multi-cohort datasets across many data types.
• Competitive Salary: Salary will be competitive compared to similar roles.

How to Apply: Send your CV, a short introduction, and details of your research experience to both Aeron Small (asmall@bwh.harvard.edu) and Zhi Yu (zyu5@mgh.harvard.edu).

Postdoctoral Fellow (closed)

The official job posting for this position at MGH can be found here.

Description: Yu Lab at MGH and the Broad Institute of MIT and Harvard is seeking one or two motivated and creative Postdoctoral Fellows to join our team (https://zhiyu7.github.io/).

Our lab focuses on developing and applying computational and statistical methods to multi-omics and other multi-modal data (including genomics, transcriptomes, proteomics, medical imaging, clinical data, and lifestyle data) to examine the mechanisms and potential interventions for cardiovascular and other age-related diseases. We aim to develop personalized strategies for disease prevention and treatment.

Research Areas & Responsibilities: The successful candidate will use their strong computational expertise to lead projects in one or more of our key research areas, depending on interest and expertise:

• Clonal Hematopoiesis & Age-related Disease: We are passionate about understanding the biology of aging. This research area focuses on how age-related somatic mutations, particularly clonal hematopoiesis (CH), contribute to disease. We believe understanding and intervening in these processes is one of the most important questions for our society.
• Machine Learning & Multi-modal Data: We are also working on leverage machine learning to extract information from imaging and clinical notes and further integrate with omics data to discover novel biological insights and perform risk prediction.
• Multi-omics Methods Development: While we apply multi-omics data to many questions, we would love to tackle fundamental methodological challenges within the data itself (e.g., discordance between proteomics platforms). We are highly motivated to develop methods that make these measurements more robust, harmonized, and interpretable.

Qualifications:

• A Ph.D. (or expected Ph.D.) in a quantitative field such as Biostatistics, Bioinformatics, Computational Biology, Electrical or Biomedical Engineering, Genetic Epidemiology, or related discipline.
• Strong computational and programming skills are essential (e.g., R or Python).
• Demonstrated experience in analyzing large-scale, complex datasets.
• First-authored publication(s) demonstrating scientific rigor and creativity.

Please Note: A background in biology or prior experience with omics data is not a requirement. As the field evolves rapidly, we welcome candidates with relevant but non-overlapping skill sets who are willing to adapt and learn, so we can grow together.

What We Offer:

• Committed Career & Research Development: We are dedicated to advancing your career. You'll receive active support to build your publication record. For postdocs aiming for academia, we provide support for grant applications (e.g., K99/R00) to ensure you become a highly competitive candidate.
• Collaborative Co-Mentorship: To broaden your expertise and support network, we can offer flexible co-mentorship opportunities.
• Access to Rich, Large-Scale Data: You will work with extensive, multi-cohort datasets encompassing a wide variety of data types.
• Develop Your Mentorship & Leadership Skills: You will have the opportunity to mentor talented and motivated junior students, which provide leadership experience and also lead to co-authorship.
• World-Class Research Environment: Of course – this is Boston : )
• Competitive Salary: Salary will be competitive compared to similar roles.
• We welcome you to chat with our current members to learn more about the group.

How to Apply: If interested, please send your CV to Zhi at zyu5@mgh.harvard.edu or zyu@broadinstitute.org

Computational Associate – Genetic Epidemiology and Statistical Genetics (closed)

Dr. Zhi Yu and Dr. Michael Honigberg are jointly seeking two motivated and skilled computational associates to join their interdisciplinary research teams. These positions offer a unique opportunity to work on cutting-edge genetic epidemiology and statistical genetics projects, with potential focuses on polygenic risk score development, rare variant analysis, genetic association studies, and classical epidemiology.

Position Overview:

Depending on expertise and interests:

• One associate will work on polygenic risk score development, rare variant analysis, and genetic association studies.
• The other will focus on classical epidemiology work.
• This role is ideal for post-bachelor or post-master students looking to gain hands-on research experience, build their publication record, and explore research opportunities before applying to PhD programs. Both Dr. Yu and Dr. Honigberg have robust publication records and are committed to providing comprehensive training, guidance in research and writing, and opportunities for collaboration and presenting research work.

Key Responsibilities:

• Perform genetic and epidemiological data analyses, with a focus on polygenic risk scores, rare variants, or classical epidemiology.
• Collaborate with an interdisciplinary team, including researchers at Massachusetts General Hospital (MGH), Harvard Medical School, and the Broad Institute of MIT and Harvard.
• Participate in scientific writing and contribute to publications.
• Present research findings at internal and external meetings.

Qualifications:

• Master’s degree in genetic epidemiology, biostatistics, statistics, or a related field (Bachelor’s degree with relevant experience will also be considered).
• (Preferred but not required) Experience analyzing genetic and genomic data, such as genome-wide association studies (GWAS) or rare variant analysis.
• Strong programming skills (e.g., R, Python).
• Excellent organizational, communication, and collaborative skills.
• Self-motivated and driven to contribute to scientific research.

Why Join Us?

This position provides the opportunity to:

• Work in a dynamic and collaborative environment at the intersection of genetics, epidemiology, and computational biology.
• Be part of an innovative research community with access to world-class resources at MGH, Harvard Medical School, and the Broad Institute.
• Receive mentorship and hands-on training from leading experts in the field.
• Develop a strong publication record, valuable for future academic or professional pursuits.

Apply: Please contact Zhi via email (zyu5@mgh.harvard.edu) with your CV, a short introduction, and details of your research experience. Salary will be competitive compared to similar roles.