Cambridge , MA 02138
The laboratories of Patrick Ellinor and Steven Lubitz at the Broad Institute are looking for exceptional computational biologists to join our efforts.
We are looking for highly motivated and talented individuals who will collaborate with other computational biologists, laboratory scientists, and clinicians in a collegial work environment characterized by informality and intellectual rigor. You will take part in analyzing data from our ongoing sequencing/genotyping studies focused on atrial fibrillation and other cardiovascular diseases, inform patient stratification using data from available resources (biobanks, genomic consortia, etc), and contribute to ongoing target validation efforts to help inform the drug discovery process.
The Broad Institute provides a vibrant research environment with close links to top academic institutions across the Boston area and provides the potential for your contributions to be used and recognized worldwide.
Apply and develop computational pipelines to run genome-wide and phenome-wide association studies.
Work with a large-scale biobank.
Duties will include defining and extracting phenotypes in collaboration with clinicians, establishing efficient computational pipeline to run association studies, fine mapping of results.
Mining of genetic databases and resources to evaluate genetic loci for drug discovery.
Conceive, implement and test statistical models; work with wet-lab researchers to translate these models into testable experiments; help analyze data from experiments.
A Master's degree in a quantitative discipline (such as computational biology, biostatistics, statistical genetics, computer science, bioinformatics, physics - or in biology with a strong quantitative background) or equivalent experience.
Experience with biological datasets, preferably large scale genotyping and/or sequencing data and experience with electronic health record (EHR) data.
Proficiency in at least one modern programming language.
Experience with a scientific programming environment, such as R, Python, Perl, or Matlab, is preferred.
Experience with publicly available genetic databases (eg GTEx, Roadmap, ExAC, UCSC, GWAS catalog) preferred.
Fast learner, analytical thinker, creative, "hands-on", team-player.
Strong communication skills.
Knowledge of cardiovascular disease is a plus but is NOT required.
Inclination to acquire such knowledge is.
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