Postdoctoral Fellow - Cardiovascular Disease Initiative (CVDi)

The Broad Institute is an amazing place - we apply our deep knowledge of human genetics to empower a revolution in biomedicine and accelerate the pace at which the world conquers disease. Through our partnerships with MIT, Harvard, and the Harvard teaching hospitals, we've become a worldwide hub of cutting-edge biomedical science. The Broad was founded to explore the applications of genomic medicine, and now conducts research in infectious disease, cancer, and inherited disease, along with basic research in the life sciences.

The Broad is looking for an exceptional postdoctoral fellow candidate to join the Ellinor laboratory within the Cardiovascular Disease Initiative (CVDi). The successful candidate will join an interdisciplinary team of computational scientists, machine learning scientists, laboratory scientists, and clinicians working together to further understand the underlying causes of cardiac diseases and contribute to identifying and validating new molecular targets in the field.

This role is anticipated to bridge clinical science and population genetics of cardiovascular disease. The ideal candidate would carry out both analysis of large scale genetic and clinical datasets. They should be facile executing analyses of genotype-array, and genome-sequencing. In addition, a focus area will be the application of modern machine learning techniques to clinical imaging data sets such as electrocardiograms or magnetic resonance imaging.

The candidate would collaborate directly with other postdoctoral fellows and scientists performing experimental research studies at the bench. They will be contributing to and leading the writing of scientific manuscripts, and working within international collaborations and potentially with industry partners.

Example work streams may include:
- Design and lead independent analytical projects
- Run genome-wide association studies on genotype array data and genome-sequencing data
- Analyze genomic and phenotypic data from the AFGen Consortium and large biobanks, such as UK Biobank, All of Us and the Million Veterans Program
- Perform downstream analyses with GWAS summary level results, including polygenic risks scores, expression quantitative trait loci, transcriptome-wide association studies, enrichment analyses, LD score regression, and gene prioritization methods
- Integrate genomics data with other available expression, sequencing, and epigenetic datasets to help prioritize potential therapeutic targets
- Perform clinical and epidemiological studies of cardiovascular disease.
- Collaborate with machine learning scientists to apply state-of-the-art deep learning models for exact phenotyping.
- Work in an interdisciplinary team, contributing to study designs in collaboration with clinicians and data scientists
- Mentor junior computational lab members
- Create scientifically rigorous visualizations, communications, and presentations of results
- Prepare scientific manuscripts, contribute to writing grants
- Help to maintain and organize computational infrastructure and resources
- Contribute to generation of protocols and intellectual property

Requirements:
- MD with clinical/epidemiological/genetic research experience or Ph.D. in a quantitative discipline such as computational biology, computer science, bioinformatics, statistics, mathematics, physics, or related field preferred, but talented applicants of all levels are encouraged to apply
- Demonstrated expertise in genomics data analysis and enthusiasm about biology, technology and cardiovascular medicine
- Experience with standard bioinformatics tools and Python, R, or an equivalent scripting language.
- Experience with either high performance computing environments or cloud based computational environments (Google Cloud, AWS, Azure) is preferred
- Experience performing genome-wide association studies (single variant and gene based tests), and quality control procedures for variant and sample filtering is a plus
- Track record of working on complex problems, and ability to integrate data from multiple disciplines
- Strong interpersonal, influencing, and collaboration skills to work in a team-oriented, matrix environment
- Outstanding personal initiative, communication skills, and the ability to work effectively as part of a team
- Outstanding verbal and written communication abilities
- A passion for science and sense of urgency to find new medicines to benefit patients