Research Fellow

About the role

The laboratory of Dr. Sarah Urbut is located within Mass General Brigham's Heart and Vascular Institute's (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.

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

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
• 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 Linux, R, Python and AWS use
• 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

Pay

The salary for this position ranges from $73,544–$81,179.

Schedule

Work will be performed in the Simches Research Building on the Massachusetts General Hospital main campus, in Boston, MA. This is an on-site position located at 185 Cambridge Street.