Postdoctoral Fellow

City of Hope • Duarte, CA

2d • Onsite

About The Position

Join the forefront of groundbreaking research at the Beckman Research Institute of City of Hope, where we're changing lives and making a real difference in the fight against cancer, diabetes, and other life-threatening illnesses. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases, shaping the future of medicine through cutting-edge research.
The LaBarge Lab at City of Hope is expanding its research program focused on preventing aging-related breast cancers. Working with mentorship from the principal investigator, you will conduct independent research to model adaptive oncogenesis and determine how age-related changes in the breast tissue microenvironment create selective pressures that favor epithelial cells harboring somatic mutations commonly associated with breast cancer. This work will integrate computational modeling with experimental cell and tissue biology to investigate how aging alters epithelial fitness, cell competition, clonal selection, and early tumor evolution. Engineered culture microenvironments, primary human mammary epithelial cells, and cell-based functional assays will be used to test and refine model predictions.

Requirements

• PhD, MD, or equivalent degree in Computational Biology, Systems Biology, Cell and Molecular Biology, Cancer Biology, Bioengineering, Applied Mathematics, Biophysics, or a related discipline. Candidates with an MD must demonstrate relevant quantitative or laboratory research experience.
• Current knowledge of cancer evolution, aging biology, epithelial cell and tissue biology, tumor microenvironments, biostatistics, and breast cancer biology.
• Experience developing or applying quantitative models to biological systems, preferably involving clonal selection, cell competition, evolutionary dynamics, population dynamics, or multicellular tissues.
• Experience analyzing complex biological datasets using R, MATLAB, Python, or a comparable programming environment.
• Experience integrating computational predictions with experimental observations and translating model outputs into biologically testable hypotheses.
• Familiarity with current experimental approaches involving cultured cells, including genetic manipulation, gene transfer, molecular cloning, gene-expression perturbation, and nucleic-acid preparation and analysis.
• Experience with, or a strong interest in learning, primary cell culture, engineered tissue microenvironments, three-dimensional culture systems, quantitative microscopy, immunofluorescence, and live-cell imaging.
• Strong written and oral communication skills and the ability to work effectively in an interdisciplinary and collaborative research environment.
• If the candidate lacks extensive experience in either computational modeling or experimental cell biology, they must demonstrate a strong commitment and capacity to develop expertise in that area.

Nice To Haves

• Experience analyzing genomic, transcriptomic, epigenomic, imaging, or cell-phenotype data is preferred.

Responsibilities

• Develop and apply computational models of adaptive oncogenesis, epithelial cell competition, and the selection of somatic mutations within aging breast tissue microenvironments.
• Integrate experimental and computational approaches to determine how age-related stromal, extracellular-matrix, biochemical, and biophysical changes influence the relative fitness and clonal expansion of mammary epithelial cells.
• Master existing, and contribute to the further development of, primary human mammary epithelial cell culture technologies, breast tissue mimetics, engineered cell culture microenvironments, and cell culture substrata.
• Design and perform competition and functional-fitness assays using epithelial cells carrying somatic mutations commonly associated with breast cancer.
• Analyze fixed and live cells in two-dimensional and three-dimensional cultures, tissue mimetics, and animal or human tissue samples to investigate the dynamics and consequences of age-dependent clonal selection. These studies may include samples from the HMDBank, clinical specimens, and archival materials.
• Generate quantitative measurements of cell proliferation, survival, differentiation, lineage state, spatial organization, and competitive fitness for use in model development and validation.
• Perform computational analysis of imaging, molecular, genomic, and phenotypic data to identify relationships among microenvironmental states, oncogenic mutations, and epithelial-cell behaviors.
• Iteratively refine computational models using experimental results and translate model predictions into testable biological hypotheses.
• Collaborate with experimental and computational investigators across disciplines, including cell biology, cancer biology, aging biology, bioengineering, biostatistics, and quantitative modeling.
• Supervise and mentor research assistants in performing experiments, analyzing data, and interpreting results.
• Conform to all laboratory cell culture, biosafety, data-management, and record-keeping practices, and maintain accurate and detailed laboratory and computational records.
• Contribute to the laboratory’s shared resources and overall progress by exchanging expertise and materials, participating in meetings and seminars, supporting neighboring laboratories, and helping maintain a safe and efficient research environment.
• Present research findings at national and international scientific conferences.
• Contribute to collaborative laboratory projects when appropriate and prepare manuscripts for publication in peer-reviewed scientific journals.

Benefits

• Comprehensive Benefits

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree