Postdoctoral Scientist - Knott Lab

Computational Postdoctoral Scientist – Spatial and Single-Cell Cancer Genomics

We are seeking a Computational Postdoctoral Scientist to analyze large-scale spatial transcriptomic, single-cell, and longitudinal molecular datasets to investigate mechanisms of tumor cell dissemination, metastatic dormancy, and metastatic awakening. The successful candidate will analyze spatial datasets from primary tumors and clinically normal tissues harboring disseminated tumor cells, with the goal of identifying tumor-intrinsic and microenvironmental features associated with dissemination and metastatic progression. The scientist will also analyze longitudinal blood-based datasets to identify systemic host changes associated with metastatic awakening. The position will involve close collaboration with wet-laboratory scientists who will experimentally test hypotheses generated from computational analyses.

To learn more, please visit: Knott Research Lab | Cedars-Sinai.

Are you ready to be a part of breakthrough research?

Working independently but in close cooperation and in consultation with the Principal Investigator 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 Job Duties and Responsibilities:

May assist in the preparation of grant proposals, but is not responsible for generating grant funds.

May participate in publications and presentations as author or co-author.

Designs and performs experiments. Will keep appropriate experimental records and documentation and analyze the results with the Principal Investigator.

May develop, adapt, and implement new research techniques and protocols.

Analyzes interpret, summarizes, and compiles data.

Performs routine and complex laboratory procedures throughout the training period.

Operates and maintains equipment and instruments.

May observe MD-patient or MD-human research subject interactions as it pertains directly to research being performed.

Department-Specific Duties and Responsibilities:

Analyze large-scale spatial transcriptomic and/or single-cell datasets.

Define tumor, immune, stromal, and other cellular states associated with dissemination and metastasis.

Perform spatial neighborhood, pathway, cell-state, and cell-cell interaction analyses.

Integrate spatial data with clinical, genomic, and longitudinal blood-derived datasets.

Develop reproducible computational workflows and publication-quality analyses.

Work with experimental scientists to prioritize and test candidate mechanisms.

Contribute to manuscripts, presentations, and grant applications.

Education:

Doctorate Degree in Cancer Biology, Immunology or a related field, required. Preferably in computational biology, bioinformatics, genomics, of cancer biology.

Experience and Skills:

Acquires thorough technical and theoretical knowledge of research project and objectives during one to five (1-5) year post-doctoral appointment.

Works independently on research projects designed by a mentor (typically the PI) within area of specialization.

Demonstrated aptitude to perform experimental protocols and procedures, including detailed data collection, and analysis, and operation and maintenance of specialized equipment.

Knowledge of safety standards and maintenance of specialized equipment.

Experience analyzing large-scale single-cell RNA-seq, spatial transcriptomic, or related high-dimensional datasets is highly desirable.

Strong computational skills in R and/or Python is preferred.

Knowledge of cancer biology is a plus.

Expertise in metastasis biology, including dissemination, dormancy, and metastatic recurrence is preferred.

Knowledge of early tumor cell dissemination and disseminated tumor cell biology is a plus.

Experience integrating spatial or single-cell data with clinical or longitudinal datasets is highly desirable.