The Chu Lab at the University of Virginia (UVA) is inviting applications for multiple Postdoctoral Researcher positions in Computational Biology and Machine Learning. The research focuses on developing advanced machine learning, generative modeling, and statistical frameworks for single-cell and spatial transcriptomics, with applications in cancer, inflammation, tissue senescence, and translational diagnostics.
Candidates from quantitative and computational disciplines such as Computer Science, Mathematics, Statistics, Physics, Engineering, and related areas are strongly encouraged to apply. Prior biology experience is not mandatory, as domain-specific knowledge can be acquired during the research program.
Postdoctoral Researcher – Computational Biology & Machine Learning
Summary Table
| Details | Information |
|---|---|
| Position | Postdoctoral Researcher |
| Research Area | Computational Biology & Machine Learning |
| Organization | University of Virginia (Chu Lab) |
| Location | Charlottesville, Virginia, USA |
| Start Date | Flexible |
| Qualification | Ph.D. in quantitative/computational discipline |
| Application Mode | Email Application |
| tchu@uva.edu |
Designation
Postdoctoral Researcher – Computational Biology & Machine Learning
Research Areas
The selected candidates will work on cutting-edge interdisciplinary research projects, including:
1. Bayesian Transcriptome Deconvolution and Gene Regulation
Development of next-generation Bayesian frameworks extending BayesPrism to integrate eQTL modeling, cell-type specific genetic effects, cis-eQTL programs, and gene regulatory networks using bulk and single-cell datasets.
2. Spatial Transcriptomics and Cell–Cell Interaction Modeling
Building deep learning frameworks for spatial transcriptomics, ligand–receptor signaling analysis, paracrine communication, and niche-dependent gene regulation in complex tissue microenvironments.
3. Cell-free RNA and Liquid Biopsy
Developing computational methods to infer cell-type origins from cfRNA in blood and urine for non-invasive diagnostics, including transplant rejection monitoring, early cancer detection, and inflammatory disease applications.
4. Modeling Cell State Transitions
Using physics-informed neural networks and Neural ODE frameworks to model gene regulatory dynamics from perturbation and time-series datasets, enabling causal regulator identification and rational in silico perturbation design.
Location
Charlottesville, Virginia, USA
Eligibility / Qualification
Required Qualification
- Ph.D. in:
- Computer Science
- Applied Mathematics
- Statistics
- Computational Biology
- Biophysics
- Engineering
- Or related quantitative disciplines
- Degree must be completed by the start date.
Preferred Skills
- Strong mathematical and statistical background
- Proficiency in Python, R, and PyTorch (or equivalent frameworks)
- At least one peer-reviewed research publication
- Strong interest in solving biological problems using quantitative approaches
Important Note
Prior biology experience is not required. Applicants from purely computational backgrounds are strongly encouraged to apply.
Job Description
The selected postdoctoral researchers will contribute to the development of machine learning, generative modeling, and statistical approaches for analyzing high-dimensional biological data. Responsibilities include:
- Designing computational algorithms and predictive models
- Developing deep learning frameworks for biological systems
- Working with transcriptomics and multimodal datasets
- Collaborating on interdisciplinary research projects
- Publishing research findings in leading journals and conferences
- Contributing to translational biomedical applications
The Chu Lab emphasizes collaborative mentorship, scientific independence, grant writing support, and conference visibility for career development.
Mentorship & Career Development
Mentorship as Collaboration
Researchers are treated as collaborators with direct involvement in scientific and technical decision-making.
Scientific Independence
Candidates are encouraged to pursue independent research directions with full institutional support.
Grant Writing Training
Support is provided for fellowship and grant proposal development.
Conference Opportunities
Full travel support is available for presenting research at leading computational biology and machine learning conferences.
How to Apply
Interested candidates should send an email to:
Email: tchu@uva.edu
Subject Line
Postdoc Application — [Your Name]
Application Documents
Applicants should include:
- Cover letter describing research experience, interests, and career goals
- Updated CV
- Contact information for three references
Last Date to Apply
Applications are open until positions are filled.
