If interested in this opportunity, applicants are encouraged to forward their CV and two letters of recommendation via email to: Emily Garcia at YEGARCIA@mgh.harvard.edu.
PLEASE REFERENCE: ‘JOB POSTING – POSTDOCTORAL FELLOW – MGH & BROAD’ in the email subject line.
Postdoctoral Research Fellow – Massachusetts General Hospital and Broad Institute
The Massachusetts General Hospital (MGH), a Harvard affiliate, is looking for exceptional candidates to join the laboratories of Drs. Steven Lubitz, and Patrick Ellinor in the MGH Cardiovascular Research Center and Broad Institute. The successful candidate will join an interdisciplinary team of epidemiologists, clinician-scientists, statistical geneticists, computational biologists, bioinformatics analysts, and biologists, who are working together to 1) understand the fundamental causes of heart disease, 2) identify individuals at risk, and 3) implement methods to improve health outcomes.
The candidate will lead projects related to heart disease, with a focus on cardiac arrhythmias. Projects will involve analysis of genomic data, human health data from electronic medical records, and development of clinical trials to test the effectiveness of interventions to improve outcomes. Projects are collaborative and span multiple institutions as well as international consortia. The candidate must thrive in an academic/professional atmosphere, where interdisciplinary teams are central to project success.
The position provides an opportunity to conduct research in an immersive and collaborative research environment studying heart diseases of substantial public health relevance. The position is ideal for a PhD level candidate with an interest in a translational academic research career.
Topical areas of focus:
Identify mechanisms of cardiac arrhythmias by analyzing large-scale genome-wide data (genotyping, whole genome sequencing, and additional ‘omics data)
Identify individuals at risk for arrhythmias and related morbidity by applying traditional and novel statistical methods (e.g., machine learning algorithms) to electronic health record data
Test the effectiveness of implementing multidimensional-derived risk-guided management strategies in the clinical environment to improve health outcomes (e.g., risk-guided screening, risk-guided treatment)
Develop study designs
Actively conduct data analyses
Lead the interpretation of data and results
Collaborate closely with team to ensure activities are within scope and timeline of project goals
Regularly communicate accomplishments and progress at project team meetings
Proactively communicate to help ensure analytical goals are achieved
Coordinate and interact closely with other scientists on data quality and file management
Actively participate in the preparation of manuscripts for publication and present at scientific conferences
Assist in peer- and student-mentorship, shares expertise, provides training and guidance as needed
Participate within a team of scientists to foster a culture of scientific excellence and collaboration
Genome-wide genotyping data in > 500K individuals
Whole genome-sequencing data from a consortium spanning > 70K individuals
Longitudinal electronic health records encompassing ~ 7 million individuals across 7 hospitals
Collaboration with a multidisciplinary team of investigators spanning the Massachusetts General Hospital, Broad Institute, and Framingham Heart Study
Ph.D. in Epidemiology, Biostatistics, Genetic Epidemiology, Statistical Genetics, or other relevant scientific discipline or equivalent experience required
Demonstrated experience designing computational methods and tools, including prior experience with algorithms relevant to genetic analysis and/or health outcomes data
Deep understanding of biostatistical and epidemiologic methods
Excellent programming skills (e.g., R, python, Linux)
Basic understanding of biology
Familiarity with clinical trial concepts
Appreciation of public health importance of heart disease and arrhythmias
Ability to work independently while making necessary connections with experts in various computational analysis groups
Mature, innovative, self-starter, highly motivated
Excellent communication, organization, and time management skills