Research Professional 1

The Chu Lab - Department of Genome Sciences, University of Virginia School of Medicine

The Chu Lab (www.tchulab.org) in the Department of Genome Sciences at the University of Virginia (UVA) School of Medicine is seeking a Research Professional 1 in computational biology and machine learning. The lab develops modern machine learning, generative modeling, and statistical learning frameworks to decipher single-cell and spatial transcriptomics data, with the goal of uncovering cellular and tissue dynamics underlying cancer, inflammation, and tissue senescence.

This is a foundation-level research position. Working under the close supervision and mentorship of the Principal Investigator, the Research Professional will contribute to the design, implementation, and benchmarking of machine learning and statistical methods, and to the analysis of single-cell and spatial omics data, while developing the specialized skills of the profession.

Salary Range: 55-65k commensurate with education and experience

• Implement, train, and evaluate machine learning, generative modeling, and statistical methods for single-cell and spatial transcriptomics, under the guidance of the PI.
• Process, analyze, and visualize single-cell and spatial omics datasets, and assist in benchmarking methods against existing approaches.
• Contribute to ongoing lab method-development projects, including coding, experimentation, and documentation of results.
• Write clean, well-documented, and reproducible code, and maintain version-controlled software.
• Present progress in lab meetings and contribute to manuscripts, software releases, and technical documentation.
• Collaborate with lab members and departmental collaborators, and learn from more experienced colleagues.

The Research Professional will support one or more of the following ongoing projects:

• Generative models of single-cell and spatial data to characterize cellular and tissue heterogeneity in cancer, inflammation, and tissue senescence.
• Neural differential equation and deep-learning models for spatial and single-cell transcriptomics to dissect cell-cell interactions and perturbation responses.
• Deep-learning and statistical deconvolution methods for inferring gene regulation from bulk, single-cell, and spatial-omics data.

Master's degree in Computer Science, Applied Mathematics, Statistics, Computational Biology, Biophysics, Engineering, Quantitative Genetics, or a related quantitative discipline, in hand by the appointment start date.

• Strong foundational knowledge in mathematics and statistics.
• Proficiency in Python and PyTorch (or an equivalent deep-learning framework).
• Experience implementing modern deep generative models (e.g., flow matching, diffusion models, normalizing flows, or variational autoencoders).
• Genuine intellectual curiosity for solving biological problems through quantitative approaches.
• Prior experience with spatial transcriptomics, single-cell omics, or related biological datasets is a plus but not required; candidates from purely computational backgrounds are encouraged to apply, and domain-specific biological knowledge can be acquired on the job.

The Chu Lab is built on the philosophy of "Mentorship as Collaboration," where team members are valued as scientific collaborators rather than assistants. You will receive individualized mentorship and direct technical engagement in algorithm and model development.

• Active Collaboration. The PI maintains an open-door policy, meets regularly with team members, and is deeply involved in supporting their algorithm and model development.
• Skill Development. You will be supported in growing your technical and scientific skills, with the opportunity to take on increasing responsibility as you develop.
• Visibility. Support for presenting at venues spanning machine learning and computational biology, and assistance in building your professional network across academia and industry.

The lab is led by Dr. Tinyi Chu, who joined UVA as Assistant Professor in 2026. Dr. Chu received his Ph.D. in Computational Biology from Cornell University and completed postdoctoral training at Memorial Sloan Kettering Cancer Center and Yale University. His work has appeared as first- or co-first-author publications in Nature Cancer, Nature Genetics, and Cell Stem Cell. He is the lead developer of BayesPrism, a Bayesian deconvolution framework selected as a Nature Cancer 2022 highlight. His research has been recognized by a Damon Runyon Quantitative Biology Fellowship and is supported by an NIH K99/R00 Pathway to Independence Award (NHGRI) and substantial UVA institutional startup funding.

The Chu Lab is part of a vibrant interdisciplinary research community at UVA, with active collaborations across the UVA School of Medicine and full access to UVA's high-performance computing resources and core facilities. Charlottesville, Virginia is a highly livable university town at the foothills of the Blue Ridge Mountains, known for its quality of life, affordability relative to other U.S. research hubs, and rich cultural and outdoor offerings.

This is a 12-month appointment with the possibility of renewal contingent upon satisfactory performance and the availability of funding.

This position will sponsor qualified applicants for work visas. The start date is available immediately and is flexible.

How to Apply

Please apply through UVA Online at UVA Careers and search for R0084733 Complete the application and upload the following required materials:

• Resume
• Cover Letter

Internal applicants may search and apply for jobs using the UVA Internal Careers website. Please note that multiple documents can be uploaded in the "Resume" box. Applications that do not contain all required documents will not receive full consideration.

For questions about the position, please contact Dr. Tinyi Chu at tchu@uva.edu. More information about the lab: www.tchulab.org.

The University will perform background checks on all new hires prior to employment.

The University of Virginia is an equal opportunity employer. All interested persons are encouraged to apply, including veterans and individuals with disabilities. Learn more about UVA's commitment to non-discrimination and equal opportunity employment.