Postdoctoral Researcher – Computational biology/Cancer bioinformatics (Full Time

The Theodorescu Lab within the University of Arizona Cancer Center (UACC) and Padi Labs within the Department of Molecular and Cellular Biology are looking to hire a joint postdoctoral researcher with a strong background in computational biology and/or cancer bioinformatics. This project will be focused on integrating rich, multimodal ‘omics data from cell lines and animal models, with the goal of identifying mechanisms leading to transformation and cancer. To identify such cancer-driving effects, we combine genomics, proteomics, and spatial transcriptomics data generated in the Theodorescu Lab with techniques for network inference, epigenetic rewiring, and dynamic modeling developed in the Padi Lab. Examples of the type of work that will be undertaken can be seen by our papers by Chen (Nature. 2025 Jun;642(8069):1041-1050), Abdel-Hafiz (Nature. 2023 Jul;619(7970):624-631), Gouin (Nature Commun. 2021; 12;12(1)), Ben Guebila (Genome Biology. 2023; 24(1):45) and Yang (Journal of Clinical Investigation. 2025; 135(7)). Our final goal is to identify novel biomarkers and interventions for cancer that will improve patient outcomes.

This individual would join two vibrant groups that closely collaborate to bridge the gap between innovative cancer research and state-of-the-art computational modeling. The successful candidate will be a driven, creative, team-oriented individual with an aptitude for quantitative/informatic methods and a passion for helping to discover fundamental cancer biology mechanisms that have potential clinical impact and thus can be moved eventually towards a clinical setting. We employ a wide variety of computational and experimental approaches and seek individuals with a strong understanding of both bioinformatics and molecular biology. experience gained in our laboratory will help the candidate to become competitive for permanent positions in either academia or industry. We welcome applications from both recent PhD or MD/PhD recipients and individuals seeking additional postdoctoral training. For more information, please visit: https://cancercenter.arizona.edu/person/dan-theodorescu-md-phd and https://www.padilab.com.

Outstanding U of A benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; UA/ASU/NAU tuition reduction for the employee and qualified family members; access to UA recreation and cultural activities; and more!

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• Design and perform quantitative data analysis. Keep detailed record of codebase with documentation and share the results with the Principal Investigators.
• Develop, adapt, and implement new research techniques and algorithms.
• Analyze, interpret, present, and interpret the data clearly and accurately.
• Perform routine and complex data analysis procedures throughout training period.
• Assist in preparation of grant proposals with the PIs but is not responsible for generating grant funds.
• Participate in publications and presentations as author or co-author.
• Meet with both PIs on a regular basis to discuss research progress and plans.
• May be asked to write small grant proposals or NRSA/T32 applications.
• Spends about 75% of time on computational analysis and 25% of time on writing articles/analyzing data/online research.

Knowledge,
Skills, and Abilities:

• Ability to work semi-independently on research projects within an area of specialization.
• Thorough technical and theoretical knowledge of research projects and the objectives to be accomplished during this post-doctoral appointment.
• Demonstrated aptitude to perform quantitative analyses, generate reproducible code, and interpret results in a biological context.
• Strong understanding of both bioinformatics and
  molecular biology.

• Doctorate
  (PhD or MD/PhD) in computational biology, physics, math, computer science, or
  related field.

• Outstanding publication record from prior graduate and/or postgraduate training and/or existing extramural funding.

Application: The online application should be completed in its entirety. Blank or missed information may be considered an incomplete submission.

Cover Letter: Should clearly indicate how your skills and professional employment experience meet the Minimum and the Preferred qualifications (if applicable).