The Broad Institute of MIT & Harvard is seeking a highly motivated Postdoctoral Associate to join the Xavier Lab and Klarman Cell Observatory and lead data analysis and algorithm development designed to determine the underlying factors contributing to health and disease, with a strong focus on discovering disease mechanisms at the level of their molecular and cellular pathways.

We are an interdisciplinary group with expertise in computational biology, functional genomics, microbiology, and immunology. The candidate for this position will be working closely with computational and experimental biologists, including Principal Investigators, graduate students, and staff scientists in the group. They will contribute by directing, planning, and executing the analyses of large multi-omic datasets with the goal of identifying biological features (e.g., variants, genes, microbes) that are relevant to human health. Prospective applicants should have made significant contributions to their area of study, as evidenced by a strong publication record. We are seeking creative and highly motivated individuals who want to work in a dynamic, multi- disciplinary research environment.

The ideal candidate will be the computational analysis lead for projects discovering the mechanisms behind inflammatory and autoimmune diseases. They should have excellent technical expertise while being driven to make a biological impact. In particular, we are seeking a postdoc to take primary leadership for analyzing spatial and single-cell multiomics datasets collected from human tissue samples as well as mouse models.

Responsibilities

• Oversee data generation and data analysis across various internal and external collaborations. Includes working datasets such as single-cell RNA-Seq, single-cell ATAC-Seq, spatial transcriptomics, proteomics, metabolomics, and/or microbiome data.

• Demonstrate flexibility to execute tasks requiring various skill levels and learn new skills as needed.

• Perform analyses using current processes and identify, propose, and implement improvements to ensure output of high-quality datasets.

• Work with other team members and leadership to plan project requirements and timelines, shift priorities as needed, and efficiently execute project tasks.

• Identify and help solve methodological problems, including adjustments to experimental and computational protocols to ensure generation of high-quality datasets suited to answer core biological questions.

• Use single cell and spatial transcriptomic approaches to understand tissue organization and how they are altered in disease environments such as chronic inflammation.

• Use a systems medicine approach to understand inflammatory and other diseases.

• Develop novel methods to facilitate exploration of unstructured datasets.

• Write well-crafted, maintainable, scalable, and performant code.

• Lead and carry out projects with minimal supervision.

• Participate in writing papers and grant proposals.

• Present project progress during group meetings.

Selected Publications