The Xia Lab recently joined the Department of Biomedical Engineering at The University of Texas at Austin, supported by the prestigious CPRIT Scholar Recruitment Award. We are building an interdisciplinary research program at the intersection of artificial intelligence, computational biology, and precision medicine, with the mission of developing next-generation AI technologies that accelerate biological discovery and transform patient care.
Our research integrates machine learning, large-scale multimodal data analysis, and experimental validation to understand human disease at single-cell resolution. We develop innovative AI methods for analyzing single-cell genomics, spatial omics, medical imaging, proteomics, and clinical data, and translate computational discoveries into biological insights through close collaborations with leading experimental biologists and clinicians.
Our lab has developed widely adopted ML/AI methods for computational biology and precision medicine, with publications in leading journals such as Nature, Nature Biotechnology, Nature Machine Intelligence, Cell Genomics, Cancer Discovery and Nature Communications.
Lab members have access to world-class computational infrastructure at the Texas Advanced Computing Center (TACC), including next-generation GPU supercomputers, as well as extensive collaborations with leading medical centers across the United States, including UT MD Anderson Cancer Center, Memorial Sloan Kettering Cancer Center, UT Southwestern Medical Center, Fred Hutchinson Cancer Center, and others.
We are committed to training the next generation of leaders in AI for biomedicine. Trainees are encouraged to pursue ambitious research questions, publish in leading journals and conferences, collaborate across disciplines, and develop an independent scientific vision that prepares them for careers in academia or industry. Postdoctoral salaries will be competitive and commensurate with experience.
Research Directions
We are particularly interested in candidates working at the intersection of AI/ML and biomedicine, including:
• AI/ML methodology for biomedical discovery
• Single-cell and spatial omics
• Computational pathology and multimodal learning
• Proteomics, metabolomics, and cancer metabolism
• Multimodal integration of molecular, imaging, and clinical data
• Strong background in machine learning, statistics, computational biology, bioinformatics, or related quantitative fields. Prior experience in biomedical applications is highly desirable but not required for candidates with strong AI/ML methodological training.
• Research experience demonstrated through publications, preprints, research projects, software, or other scholarly outputs. First-author publications are highly desirable, particularly for postdoctoral applicants.
• Experience in one or more areas relevant to our research, such as AI/ML methodology, medical imaging, single-cell or spatial omics, proteomics, metabolomics, or multimodal data integration.
• Strong motivation for research and interest in interdisciplinary collaboration.
If you are interested, please send a cover letter and CV to zheng.xia@austin.utexas.edu.
In your cover letter, please briefly describe:
1. Your most significant research contribution;
2. The research questions you hope to pursue during your PhD or postdoctoral training; and
3. Why your research interests align with the position.