Research Professional 1

University of Virginia • Charlottesville, VA

4d • $55,000 - $65,000 • Onsite

About The Position

The Chu Lab 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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• Individualized mentorship
• Direct technical engagement in algorithm and model development
• Support for presenting at venues spanning machine learning and computational biology
• Assistance in building professional network across academia and industry
• Access to UVA’s high-performance computing resources and core facilities
• Possibility of renewal contingent upon satisfactory performance and availability of funding
• Sponsorship for qualified applicants for work visas

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level