Postdoctoral Associate in Computational Multi-Omics for Immunotherapy

The Morrissy Lab is seeking a highly motivated scientist with expertise in bioinformatics and data science to join our team as a Postdoctoral Associate focused on understanding immunotherapy responses in solid tumors. The candidate will use state-of-the-art analytic approaches to investigate multi-omics data and derive an in-depth understanding of tumor ecosystem dynamics in clinical samples from immunotherapy trials – including phenotypic and functional cell heterogeneity, tumor and microenvironment cell niches, and cellular crosstalk. This research is primarily focused on cohorts of single-patient and phase I clinical trial participants treated with CAR T-cells (see Zemp el, 2024, in revision at Nature; preprint on medRxiv), and identifying mechanisms of immunotherapy response and resistance from longitudinally-collected blood and tumor tissue samples. The candidate will employ innovative bioinformatics and machine learning approaches to integrate single cell and spatial datasets from clinical samples with data from ongoing preclinical studies, further incorporating genomics and proteomics profiles from in-house or public repositories.

Job Responsibilities and Learning Objectives:

The Postdoctoral Associate will lead analyses of multi-omic datasets from patient samples, with a focus on single cell and spatial datasets. They will use statistical and machine learning approaches to interpret data in the context of tumor evolution and therapy response, with the aims of understanding: (1) dynamics of tumor growth, relapse, and resistance to current immunotherapies, (2) CAR T-cell clonal expansions and functional phenotypes throughout treatment, and (3) opportunities for novel development of next-generation CAR T products.

The Postdoctoral Associate will design and carry out computational experiments, mastering skills related to experimental design and data analysis for several technology platforms (single cell, spatial, genomics, proteomics). They will learn and master skills for integrative analyses, and attain depth of knowledge in cancer biology, cancer genomics and immunogenomics, and bioinformatics.

The candidate will lead writing of scientific manuscripts and contribute to training of more junior team members. They will develop skills in networking, mentorship, grant writing, and other areas of professional development that will boost the candidate’s career trajectory.

Individuals with a PhD degree in in bioinformatics, computational biology, biostatistics, or data science are encouraged to apply. A strong track record conducting computational data analyses (genomics/multi-omics) is required, as is proficiency in R or python. Experience working in a high-performance computing environment is highly preferred. Expertise with spatial transcriptomics or single cell data is highly valuable. Understanding of cancer biology or immunology is also an asset. Evidence of scientific accomplishment via peer-reviewed publications, and excellent verbal and written communication skills are required. Organizational and problem-solving skills are integral to the position.