Are you ready to be a part of breakthrough research?
The Omar Lab at Cedars-Sinai focuses on developing robust artificial intelligence (AI)-powered multimodal risk-stratification tools to forecast the risk of disease progression in cancer patients. These tools incorporates the composition of the tumor microenvironment in their training to improve their robustness, accuracy, and generalizability to different patient cohorts.
We are seeking a wet lab-focused postdoctoral scientist with expertise in cancer molecular biology to work on a fully-funded project aiming to investigate the mechanisms underlying bone metastasis in prostate cancer. This project leverages state-of-the-art high-resolution spatially resolved multiomics profiling of tissue samples from genetically engineered mouse models and human patients to characterize the molecular and spatial interactions between different cell types in the microenvironments of both the primary tumor and bone at different disease stages.
Working independently but in close cooperation and in consultation with Dr. Omar and other Research Scientists, the Postdoctoral Scientist will perform routine and complex laboratory procedures throughout training period. May develop, adapt, and implement new research techniques and protocols. Analyzes and interprets data. May assist in preparation of grant proposals. Participates in publications and presentations as author or co-author. Not responsible for generating grant funds.
Primary Duties and Responsibilities:
Education:
Experience and Skills:
The ideal candidate will have experience in utilizing single cell and spatial omics to decipher the cellular composition of the tumor and bone microenvironments during disease progression from localized to locally-advanced and metastatic stages. Characterizing the different cell types and interactions mediating such transitions. The ideal candidate may also have strong experience in genetically-engineered mouse models of prostate cancer, and should be proficient in the handling of tissue specimens, data collection, and interpretation (spatial transcriptomics and proteomics). They may also be skilled in the computational analysis of such data using standard libraries such as Seurat, Scanpy, Squidpy, etc. Competitive candidates will have experience publishing at least one first author paper in the mentioned disciplines as a graduate student.