Healthcare F-1 STEM OPT Sponsorship Jobs in Massachusetts

INTRODUCTION

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.

The laboratory of Dr. Michael Honigberg at the Massachusetts General Hospital Cardiovascular Research Center (CVRC) and Broad Institute of MIT & Harvard has a unique post-doctoral fellow position open for a highly qualified applicant interested in investigation at the intersection of cardiovascular, reproductive, and aging biology. The new hire will be appointed as a Research Fellow at the Massachusetts General Hospital and Post-Doctoral Fellow at Harvard Medical School.

The Honigberg lab aims to advance mechanistic understanding of emerging cardiovascular risk factors and inform new paradigms for health promotion and disease prevention. Current projects focus on pregnancy (e.g., preeclampsia/eclampsia), menopause, aging biology, and novel lipid-modifying therapies, among other topics.

This position will enable access to resources and opportunities across multiple world-class institutions. Individuals will work with a range of data types, including genetic datasets, including genotype arrays, whole exome and whole genome sequencing, single-cell sequencing, and multi-omic data (e.g., metabolic, transcriptomic, proteomic, methylation). The successful candidate will work within a dynamic and highly collaborative environment alongside computational biologists, bioinformaticians, epidemiologists, clinicians, research coordinators, students, and medical trainees.

ROLE AND RESPONSIBILITIES

The successful candidate will work closely with Dr. Honigberg, members of the Honigberg Lab, and other trainees and faculty at the MGH CVRC and Broad Institute. The successful candidate will be closely supervised by Dr. Honigberg. The successful candidate will have robust facility in computational genetics and bioinformatics. Projects will range from genomic discovery and in silico investigations of genetic mechanisms of disease, risk prediction, phenotype curation using electronic health record data, multi-omic analysis, and Mendelian randomization for causal inference.

Overall, this is a unique opportunity to engage in cutting edge science and make a central contribution to biomedical research. In addition, MGH and the Broad Institute provide vibrant research environments with close links to top academic and industry networks across the Greater Boston area and the world.

Key Responsibilities

• Construction and implementation of cloud-based pipelines for genomic, polygenic risk scoring, and biostatistical analyses.
• Processing and quality control of next-generation sequencing data.
• Processing and quality control of multi-omics data.
• Statistical analyses of genotype-phenotype association analyses, with summarization and graphical representations.
• Organizing, manipulating, and harmonizing new datasets across different formats and synchronization with existing datasets and databases.
• Phenotypic curation from electronic health record structured and unstructured data.
• Construction, implementation, and sensitivity analyses of biostatistical models in classical epidemiology, genetic epidemiology, and machine learning.
• Lead and contribute to manuscript preparation as well as internal and external project-team reports.
• Actively participate and present in project meetings and lab meetings.

BASIC QUALIFICATIONS

• Doctoral degree in computational biology, biomedical informatics, biostatistics, statistical genetics, genetic epidemiology, or computer science.
• First (or co-first) author of one or more peer-reviewed scientific publications.
• Excellent English verbal and written communication skills.
• Able to work both independently and in a team.
• Strong record of productivity, motivation, adaptability, and collaboration.
• Exceptional oral and written communication skills.
• Strong background in computational biology and bioinformatics.
• Strong skills in statistical analyses are highly preferred.
• Strong demonstrable proficiency in UNIX, R, Python, and Perl; facility with Java, Matlab, C, C++ preferred.
• Strong facility with cloud computing.
• Prior experience in human genetic analyses and bioinformatics analyses of publicly available datasets.
• Familiarity with next-generation sequence data analysis tools strongly preferred.
• Ability to adapt to rapidly changing and high-demand environments.
• Knowledge of cardiovascular disease is not required.

Qualified candidates should send a CV, a cover letter, and contact information for three references to Dr. Michael Honigberg (MHONIGBERG@MGH.HARVARD.EDU).

The expected annual salary range will be commensurate with the candidate’s experience and qualifications, and institutional guidelines.

Review of applications will begin immediately and continue until the position is filled.

EEO Statement

Massachusetts General Hospital is an Equal Opportunity Employer. By embracing diverse skills, perspectives and ideas, we choose to lead. Applications from protected veterans and individuals with disabilities are strongly encouraged.

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Remote Type

Onsite

Work Location

185 Cambridge Street

Scheduled Weekly Hours

40

Employee Type

Regular

Work Shift

Day (United States of America)

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.