Computational Scientist l - Stanley Center and Psychiatric Disease

Description & Requirements

The Broad Institute of MIT & Harvard, a world leader in genomics research, is looking for an exceptional candidate to join a research program that will use large biobank data, produced in our collaborations with Finland and other initiatives.

FinnGen is a unique research project that aims to combine genomic information of 500,000 participants with their healthcare data from national registries in search of the next breakthroughs in disease prevention, diagnosis and treatment The FinnGen project is public-private partnership involving 14 large pharmaceutical companies, the Finnish government and nine Finnish biobanks and their respective hospitals, universities and research institutions.

We are looking for computational scientists with experience in large scale statistical genetic analyses (e.g., GWAS, omics, colocalization) to join the FinnGen data analysis team housed at the Broad Institute. The FinnGen Data analysis team constructs and implements analysis pipelines for the core project analyses and provides support for more tailored analyses together with other FinnGen production teams. The Data Analysis Team has members both at Broad and at the Institute for Molecular Medicine Finland at the University of Helsinki that work closely together. The successful candidate will join an interdisciplinary team of computational biologists, bioinformaticians, software engineers, geneticists, and clinicians.

The candidate will perform large scale genetic data analyses utilizing Finnish national health care registries to understand how genetic variation contributes to various disease outcomes throughout individual lifetime. Proteomics and longitudinal clinical lab values will be a specific focus in the next phase of FinnGen in both understanding the functional mechanisms of disease associated variants, as well as in evaluating proteins as predictors of incident disease or as biomarkers of disease progression. A unique opportunity in FinnGen is the availability of clinical longitudinal lab measurements for each FinnGen individual for the past 10 years, however many analyses will be conducted across multiple biobank resources such as UK Biobank and AllOfUs..

Responsibilities

• Lead genetics/genomics data-analysis with FinnGen genotype and rich longitudinal phenotype data (e.g. execution of genome/phenome wide association studies, finemapping, colocalization with labQTL/eQTL/pQTL/other phenotypes)
• Quality control and analysis of various omics data, especially proteomics
• Develop data-analysis pipelines for robustly and reproducibly analyze very large data sets
• Under general direction, develop innovative analytical methods to enable collaborators to interpret results and design follow-up research
• Regularly communicate accomplishments and progress at project team meetings.
• Lead and participate in preparation of manuscripts for publication, prepare reports and present at scientific conferences.
• Work with the FinnGen production groups in Helsinki and at the Broad to develop analytical methodologies that will be widely distributed and shared with the scientific community utilizing FinnGen data and beyond
  
  

Requirements

• PhD in computational biology, bioinformatics, statistics or other similar quantitative discipline with 0-2 years of post-doctoral experience
• Experience in statistical genetic analysis of GWAS data
• Relevant publications in high impact scientific journals.
• Good applied statistics knowledge
• Good general programming skills and familiarity with tools of the trade (Linux, shell scripting, git ) are must have skills. The team currently works mostly with Python and R.
• Experience with biological datasets, preferably large-scale genotyping and/or sequencing data and experience with electronic health record (EHR) are desirable
• Demonstrated capability as highly organized, a creative problem-solver, detail-oriented, self-motivated, and able to work independently as well as within cross-functional teams
• Ability to adapt to rapidly changing and high-demand environments
• Cloud computing experience (Google Cloud) and familiarity with Docker are an asset.