Research Fellow

massgeneralbrigham; Boston (MA); Sur place; USD 65 000 - 90 000; Plein temps

Résumé du poste

Mass General Brigham invites applications for a Research Fellow in the Urbut Lab within the Cardiovascular Research Center at MGB. The role focuses on dynamic modeling using multimodal data and causal inference for Bayesian analysis of EHR across cohorts and clinical trials.

Ideal candidates hold a doctoral degree in computational biology or related fields and have strong experience with genomic data, multi-omics, and statistical genetics.

Qualifications

• Doctoral degree required to onboard.
• First (or co-first) author of peer-reviewed publications.
• Excellent English verbal and written communication.
• Ability to work independently and in a team.

Responsabilités

• Construct cloud-based pipelines for genomic and biostatistical analyses.
• Process and quality-control next-generation sequencing and multi-omics data.
• Perform genotype-phenotype association analyses and provide visual summaries.
• Lead manuscript preparation and project-team reporting.
• Present findings in project meetings.

Connaissances

English communication

Teamwork

Independent work

Collaboration

Formation

Doctoral degree in computational biology/biomedical informatics/biostatistics/genetic epidemiology or computer science

Description du poste

Site: The General Hospital Corporation

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

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.

Please visit surbut.github.io/Urbut_lab (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 research. In 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.

Work will be performed in the Simches Research Building on the Massachusetts General Hospital main campus, in Boston, MA.

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.

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 sta