Research Professional

Position: Research Professional 1
# Research Professional 1

University of Virginia Be an Early Applicant Full Timemid Charlottesville, Virginia, USPosted Today##

Role Overview University of Virginia is hiring a mid-level Research Professional
1. This is a full-time role in Charlottesville. posted today. applications are still in the early window, before most candidates have applied. Full responsibilities, required qualifications, and the apply link are listed in the description below.## Resume Keywords to Include Make sure these keywords appear in your resume to improve ATS scoring

Python Py Torch ORChu Lab Department Genome Sciences Sign  up free to auto-tailor your resume with all these keywords and get a higher ATS score##

Job Description The Chu Lab — Department of Genome Sciences, University of Virginia School of Medicine The Chu Lab ((Use the "Apply for this Job" box below).) 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### Primary 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.

Research Directions 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.

Minimum Qualifications 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.###

Preferred Qualifications
* 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.

Mentorship and Career Development 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…