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  • Postdoctoral Research Associate

    University of North Carolina- Chapel Hill (Chapel Hill, NC)



    Apply Now

    Vacancy ID: PDS004588

    Position Summary/Description:

    Dr. Jun Li’s lab (https://jlilab.org) at the University of North Carolina at Chapel Hill is recruiting postdoctoral researchers to join his interdisciplinary team. The lab focuses on integrative cancer omics, functional proteomics, and AI-driven bioinformatics tool development, with the overarching goal of advancing cancer treatment through data-driven translational research. Our work bridges data science and oncology by pursuing two main directions: (1) leveraging clinical and multi-omics data to generate hypotheses, uncover novel cancer vulnerabilities, and optimize therapeutic strategies, and (2) developing AI-powered analytical platforms to support patient-centered biomedical research. Current projects involve building computational frameworks for multi-omics integration, modeling cancer vulnerabilities, designing tools for data interpretation and visualization, and/or training foundation models and large language models (LLMs) for diverse biomedical applications. Dr. Li has published over 70 peer-reviewed articles with more than 53,000 citations (H-index: 56), including first-author papers in Cancer Cell, Nature Cancer, Nature Methods, Nature Communications, and Science Advances, reflecting the lab’s strong track record in high-impact cancer research. This position offers a dynamic and collaborative environment with ample opportunities for high-impact research, technical innovation, and professional growth.

    Education and Experience:

    - Solid understanding of cancer biology and omics technologies (e.g., genomics, proteomics, and/or metabolomics).

     

    - Strong publication record in relevant fields.

     

    - Proven ability to develop bioinformatics tools or AI/ML models.

     

    - Experience in integrating multi-omics data from tumors and preclinical models.

     

    - Familiarity with training or fine-tuning large-scale models (e.g., LLMs, foundation models, etc.).

     


    Apply Now



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