Postdoctoral Fellow

About the role

The LaBarge Lab at City of Hope is expanding its research program focused on preventing aging-related breast cancers. 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.

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.
• 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.

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

Qualifications

• Strong background in computational biology, systems biology, cell and molecular biology, cancer biology, bioengineering, applied mathematics, biophysics, or a related field.
• Experience with computational modeling and experimental cell biology.
• Knowledge of cancer evolution, aging biology, epithelial cell and tissue biology, tumor microenvironments, biostatistics, and breast cancer biology.
• Ability to analyze complex biological datasets using programming environments like R, MATLAB, or Python.
• Experience with primary cell culture, engineered tissue microenvironments, three-dimensional culture systems, quantitative microscopy, immunofluorescence, and live-cell imaging.
• Experience with genomic, transcriptomic, epigenomic, imaging, or cell-phenotype data analysis.
• Excellent written and oral communication skills.
• Commitment to developing expertise in computational modeling or experimental cell biology if lacking extensive experience in one area.

Skills

• Computational modeling of biological systems.
• Experimental cell biology techniques.
• Data analysis using programming languages like R, MATLAB, or Python.
• Primary cell culture and engineered tissue microenvironments.
• Quantitative microscopy, immunofluorescence, and live-cell imaging.
• Genomic, transcriptomic, epigenomic, imaging, and cell-phenotype data analysis.
• Effective communication and collaboration skills.

Benefits

City of Hope offers a comprehensive benefits package, including health insurance, retirement plans, and paid time off.

Pay

Salary is based on experience, qualifications, and location.

Schedule

Full-time position.

Contact

To apply, please visit our website and submit your resume and cover letter.