Postdoctoral Research Associates in Cancer Biology and Single-Cell​/Spatial Omics

Position Summary

The Cao Lab in the Department of Bioengineering at The University of Texas at Dallas is recruiting postdoctoral research associates in two complementary areas: (1) experimental cancer biology and tumor microenvironment research, and (2) computational single-cell and spatial omics. Candidates will work in a multidisciplinary environment that integrates cancer biology, engineered tissue models, quantitative imaging, single-cell/spatial genomics, and computational analysis. We are particularly interested in scientists who want to take intellectual ownership of a project, formulate hypotheses, interpret data critically, and drive work from initial observations to publication.

Candidates may apply to one or both research tracks.

This postdoc will take scientific ownership of projects investigating how spatial organization, cell-cell interactions, and the physical microenvironment regulate tumor cell states and therapeutic responses. Projects use human cancer models and engineered tumor microenvironments to study interactions among tumor cells, fibroblasts, and immune populations. Approaches include 3D cell culture, patient-derived models, biomaterials or bioprinting, quantitative microscopy, perturbation experiments, and single-cell or spatial profiling.

• Ph.D. in cancer biology, cell biology, tumor immunology, biomedical engineering, molecular biology, or a related field.
• Strong conceptual foundation in cancer biology and the tumor microenvironment; experience with tumor-stromal or tumor-immune interactions is highly desirable.
• Demonstrated ability to design experiments, interpret unexpected results, integrate findings with the literature, and define logical next steps rather than only execute established protocols.
• Hands-on experience with mammalian cell culture and quantitative biological assays. Experience with 3D culture, organoids/patient-derived models, imaging, biomaterials, macrophages, fibroblasts, T cells, HNSCC, or breast cancer is a plus.
• Ability and motivation to lead a project toward manuscripts and presentations, including figure preparation and scientific writing.

This postdoc will lead computational analysis of single-cell and spatial datasets across two major research directions: cancer microenvironment biology and developmental/evolutionary genomics. Projects include tumor cell-state transitions, tumor-immune-stromal interactions, spatial organization of tissues, developmental lineage dynamics, gene regulatory networks, and regulatory evolution. The candidate will work closely with experimental researchers while also developing independent biological questions from high-dimensional data.

• Ph.D. in bioinformatics, computational biology, genomics, systems biology, quantitative biology, or a related field.
• Strong proficiency in R and/or Python and experience analyzing single-cell RNA-seq data; experience with scATAC-seq, multi-omics, or spatial transcriptomics/imaging is highly desirable.
• Experience with methods such as cell-state and differential expression analysis, trajectory/lineage analysis, gene regulatory network inference, cell-cell communication, spatial neighborhood analysis, or multimodal integration is a plus.
• Strong statistical reasoning and experience building reproducible analysis workflows for large sequencing or imaging datasets.
• Ability to translate computational results into biological interpretations, testable hypotheses, publication-quality figures, and manuscripts.

• A strong publication record appropriate to career stage; first-author research publications are preferred.
• Scientific independence, curiosity, and the ability to prioritize the experiments or analyses that are most informative for the biological question.
• Strong verbal and written communication skills and the ability to work productively across biology, engineering, imaging, and computation.
• Recent Ph.D. graduates and candidates completing their Ph.D. are encouraged to apply.
• We value depth of scientific reasoning, project ownership, and the ability to interpret data over simply having experience with a long list of techniques.

The Cao Lab at UT Dallas integrates single-cell and spatial multi-omics, quantitative imaging, engineered tissue models, and computational biology to investigate cell-state transitions, gene regulatory…