Description & Requirements
COMPUTATIONAL BIOLOGIST – Mootha Laboratory
Join the Mootha Laboratory at the Broad Institute to uncover the mechanisms of mitochondrial dysfunction in human disease. As a Ph.D. Data Scientist, you will join a dynamic team of ~25 post-docs and professional scientists dedicated to high-impact discovery. We’d love to get to know you better! We highly encourage including a cover letter with your application. To help us understand your fit, please use your letter to explicitly connect your previous research publications to the mission of the Mootha Laboratory. Specifically, we are looking for a narrative on how your past methodological or biological findings align with our focus on mitochondrial systems and multi-omics integration.
Your role will involve analyzing and integrating high-dimensional multi-omics data (DNA sequencing, RNA-seq, single cell RNA-seq, proteomics, and metabolomics). You will work closely with lab members to influence all aspects of a project’s lifecycle, from initial experimental design and data capture, to quality control, statistical inference, and biological interpretation.
As part of the Broad Institute, you will have access to world-class computational infrastructure and a vibrant community of over 500 scientists. This is a rare opportunity to be part of an exciting team of leaders in biochemistry, computational biology, and clinical medicine, with ample opportunities for co-authorship on high-impact publications.
REQUIREMENTS:
• Education: Ph.D. in Bioinformatics, Computational Biology, Computer Science, Mathematics or related quantitative field
• Experience: 3+ years of research experience (inclusive of doctoral training) with a proven track record of applying multivariate statistics and integrative approaches to complex biological datasets
• Technical skills: Deep expertise in scientific programming (e.g. R or Python); experience with cloud computing (e.g. GCP/AWS/Terra.bio) and reproducible research practices is preferred
• Communication: Excellent oral and written communication skills, with the ability to translate statistics and complex data findings into actionable biological insights for experimental collaborators