The University of Texas MD Anderson Cancer Center
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Postdoctoral Fellow in GI Medical Oncology Research and Multimodal AI The University of Texas MD Anderson Cancer Center in United States
Degree Level
Postdoc
Field of study
Oncology
Funding
Competitive compensation and benefits in accordance with NIH and MD Anderson guidelines. Access to rich cancer datasets and a well-resourced collaborative research environment are highlighted; no specific stipend amount is stated.
Country
United States
University
The University of Texas MD Anderson Cancer Center
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About this position
The University of Texas MD Anderson Cancer Center is recruiting a Postdoctoral Fellow for the Department of Gastrointestinal Medical Oncology in Houston, Texas. The role focuses on advancing foundational artificial intelligence and multimodal AI for oncology within MD Anderson’s Moon Shots Program, with an emphasis on translational impact and precision oncology.
The fellow will work with large-scale cancer datasets and integrate diverse data sources such as electronic health records, digital pathology images, radiology data, bulk and single-cell omics, and real-world clinical outcomes. Research directions include therapy response modeling, treatment optimization, clinical trial matching, and patient care enhancement. The position involves collaboration with clinicians, computational scientists, biologists, and disease groups across MD Anderson, plus dissemination through publications and conference presentations.
Eligibility highlights: a PhD in Computer Science, Computational Biology, Bioinformatics, Electrical Engineering, Biomedical Engineering, or a related quantitative field; strong machine learning/deep learning background; Python proficiency; and experience with biological or clinical high-dimensional data. Preferred experience includes oncology, cancer biology, immunology, translational research, foundational models, self-supervised learning, distributed training, EHR data, digital pathology, and multi-omics integration.
Funding: competitive compensation and benefits are offered in line with NIH and MD Anderson guidelines. The posting does not list a specific stipend amount or deadline.
How to apply: use the APPLY button on the MD Anderson job page and submit through the careers portal. Review the full posting for details before applying.
Funding details
Competitive compensation and benefits in accordance with NIH and MD Anderson guidelines. Access to rich cancer datasets and a well-resourced collaborative research environment are highlighted; no specific stipend amount is stated.
What's required
PhD in Computer Science, Computational Biology, Bioinformatics, Electrical Engineering, Biomedical Engineering, or a related quantitative discipline. Demonstrated expertise in machine learning or deep learning, including familiarity with large language models, multimodal architectures, or generative AI. Proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow, or JAX. Experience working with biological, clinical, or other high-dimensional datasets. Preferred background includes oncology, cancer biology, immunology, translational research, foundational model development, self-supervised learning, distributed training, EHR data structures, digital pathology workflows, multi-omics integration, and a strong publication record.
How to apply
Apply through the job posting on the MD Anderson careers site using the APPLY button. Review the eligibility requirements and submit the application materials through the portal.
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