Research Fellow

Brain cancers are one of our greatest challenges in cancer research, due to the immense complexity of their tumor microenvironments. While immunotherapies have shown great promise in other cancers, brain cancers are stubbornly resistant. 

The College of Pharmacy and the Watson Lab is recruiting a computational postdoctoral fellow to help crack this problem using the latest approaches to spatial multi-omics to address this complexity, and find ways to reprogram the microenvironment for better responses to new immunotherapies.

The lab's primary focus is combining single cell spatial transcriptomics and spatial proteomics with interrogation of the extracellular matrix microenvironment, studying how fibrosis and mechanical forces impair treatment, and how they can be targets for new therapeutics. The Fellow will work in close partnership with the lab's experimental team to build and apply analytical frameworks that translate these data into mechanistic insight and therapeutic hypotheses. As part of the Bioinnovations in Brain Cancer initiative, the Fellow will be part of a broad collaborative environment to push state-of-the-art cancer research.

This is a founding position in a new lab with dedicated startup and international funding. As such, the Research Fellow will have the opportunity to shape the direction of this cutting-edge program at University of Michigan Ann Arbor.

Responsibilities:

• Develop and apply computational pipelines for multiplex imaging, spatial transcriptomics, single cell RNAseq, and multi-omics data integration.
• Lead graph-based network and machine learning analyses of tumor immune microenvironment architecture.
• Collaborate with wet lab team members to design experiments informed by computational findings.
• Contribute to manuscript preparation, grant writing, and presentation at national and international conferences.

Required Qualifications:

• PhD in computational biology, bioinformatics, data science, or a related quantitative field.
• Proficiency in Python and/or R; experience with high-performance computing environments.
• Experience with single cell sequencing or spatial omics data strongly preferred.
• Strong publication record and written communication skills.

Preferred Qualifications:

• Expert in graph based, multiple instance learning, large language, and fusion modeling.
• A grounding in cancer biology or immunology.
• Collaborative, self-directed, and intellectually curious.

Please submit your CV, cover letter, and minimum two letters of reference to