The laboratory of Dr. Pradeep Natarajan at the Massachusetts General Hospital Cardiovascular Research Center (CVRC) and Center for Genomic Medicine (CGM) and Broad Institute of Harvard & MIT has a unique post-doctoral fellow position open for a highly qualified applicant interested in investigating the genetics and biology of cardiovascular diseases using human genetics across diverse epidemiological cohorts, hospital-based biobanks, and within clinical trials. The new hire will be appointed as a Research Fellow at the Massachusetts General Hospital and Post-Doc at the 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 http://natarajanlab.mgh.harvard.edu 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 us to pursue highly innovative research studies to guide future therapeutic development and diagnostic tools.

The successful candidate will work closely with members of the Natarajan Lab as well as Dr. Natarajan directly. The successful candidate will be closely supervised by Dr. Natarajan. 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, polygenic risk scoring and risk prediction, 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, 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 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.

The ideal candidate should have received (or expect to receive soon) a doctoral degree.

• 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 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.

Not Applicable

Doctoral Degree and skills noted above

Not Applicable

Not Applicable

Simches Research Building