Research Fellow
Job Location:
Boston, MA - USA
Yearly Salary:
Posted:
Application Deadline:
Vacancies:
Job Summary
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.
Job Summary
APPLY BY EMAIL ONLY - APPLICATION INSTRUCTIONS BELOWThe laboratory of Dr. Sarah Urbut is located within Mass General Brighams Heart and Vascular Institutes (MGB HVI) Cardiovascular Research Center (CVRC) and also is affiliated with the Broad Institute of Harvard & MIT and Harvard Medical School. Dr. Urbut leads cutting-edge research in principled method development to integrate electronic health records (EHR) with genetics for both prediction and discovery.
The Urbut Lab has a unique research fellow position open for a highly qualified applicant interested in dynamic modeling using multimodal data and causal inference for Bayesian analysis of EHR across cohorts hospital-based biobanks and within clinical trials. The new hire will be appointed as a Research Fellow at the Massachusetts General Hospital and at Harvard Medical School.
This position will leverage stimulating environments and resources across world-class institutions. Individuals will work with a range of genetic datasets including genome-wide arrays whole exome sequencing and whole genome sequencing as well as multi-omics data (e.g. metabolic transcriptomic proteomic methylation). The successful candidate(s) will join an interdisciplinary team of computational biologists bioinformatics analysts epidemiologists physicians nurses clinical research coordinators and students.
Please visit (in process) for additional details about prior ongoing and future research. The high prevalence of cardiovascular disease the leading cause of premature death worldwide coupled with increasingly massive patient and genetic data has motivated the Urbut Lab to pursue highly innovative research studies to guide future therapeutic development and diagnostic tools.
The successful candidate will work closely with members of the Urbut Lab as well as Dr. Urbut directly. The successful candidate will be closely supervised by Dr. Urbut. The successful candidate will have robust facility in computational genetics and biostatistics. Projects will range from genomic discovery and in silico investigations of genetic mechanisms of disease risk prediction digital twin development and genomic discovery through multi-feature hypothesis-generating studies with multi-omics imaging and electronic health records.
Overall this is a unique opportunity to engage in cutting-edge science and make a central contribution to biomedical addition Mass General Brigham Massachusetts General Hospital 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 sequence 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 robust synchronization with existing datasets and databases.
Phenotypic derivation 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.
Work will be performed in the Simches Research Building on the Massachusetts General Hospital main campus in Boston MA.
Qualifications
The ideal candidate should have received (or expect to receive soon) a doctoral degree. A doctoral degree will be required to complete onboarding.
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
Doctoral degree in computational biology biomedical informatics biostatistics statistical genetics genetic epidemiology or computer science.
Strong record of productivity motivation adaptability and collaboration.
Exceptionaloral and written communication skills.
Strong background in computational biologyandbioinformatics.
Strong skills in statistical analyses are highly preferred.
Strongdemonstrableproficiency in Linus R python and AWS use.
Strong facilitywith cloud computing.
Priorexperience 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.
The salary for this position ranges from $73544-$81179.
Additional Job Details (if applicable)
IMPORTANT APPLICATION INSTRUCTIONS
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EEO Statement:
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.
About Company
Patients at Mass General have access to a vast network of physicians, nearly all of whom are Harvard Medical School faculty and many of whom are leaders within their fields.
