Extracellular Vesicles Jobs in Virginia (NOW HIRING)

Fully funded PhD Graduate Research Assistantship at Old Dominion University (ODU).

This position must be filled urgently for Fall 2026; therefore, we are looking only for U.S. citizens and permanent residents.

Join a translational research team led by Prof. Colunga Biancatelli in building machine-learning-powered digital twins for regenerative medicine on a modern Google Cloud / Vertex AI stack. Ideal for candidates in bioengineering, biomedical engineering, or computational science who want to work at the intersection of ML, mechanistic modeling, and real biomedical impact.

We are seeking a motivated PhD student to serve as the primary data engineer and modeler for a funded research project developing predictive digital twins of biological systems. In this role, you will own the end-to-end data and modeling pipeline, from curating and harmonizing complex experimental datasets to building, validating, and deploying machine-learning models in the cloud. You will work closely with an interdisciplinary team spanning computational science, bioengineering, and biomedical research.

This position is funded by the Forward Focuses Centennial Research Initiative Grant of Old Dominion University to celebrate ODU's 100th Birthday.

The GRA will work across digital modeling and of wet lab experimentation pipeline, including cell culture.

Key Responsibilities

●    Wet lab experimentation including cell culture, extracellular vesicles isolation, characterization (NTA/TRPS), WB (CD9, CD63, TSG01, Alix), RNA extraction, miRNA profiles, and functional assays (barrier function, scratch assay, polarization) within human endothelial cells and T lymphocytes.

●   Design and maintain the digital-twin data pipeline: data curation, harmonization, controlled vocabularies, and quality control.

●    Build and validate machine-learning models for small-data regimes, including Gaussian Process regression (multi-output), gradient-boosted trees (XGBoost), and calibrated uncertainty quantification.

●   Contribute to manuscripts, technical reports, and grant deliverables, and present findings to the research team and collaborators.

Required Qualifications

●    Wet lab expertise in EV research (WB, PCR, NTA, RNAseq)

●    Excellent written and verbal communication skills and the ability to work in an interdisciplinary team.

●    Foundation in machine learning and statistics, with an interest in uncertainty quantification and small-data methods.

●    Experience with data engineering and reproducible, versioned data workflows.

Preferred Qualifications

●    Hands-on experience with cloud MLOps, especially Google Vertex AI, BigQuery, and Kubeflow Pipelines.

●    Domain literacy in multi-omic (RNA-seq), biomaterials, or extracellular vesicle (EV) / bioassay data.

●    Background or interest in tissue engineering and regenerative medicine.

●    A track record of peer-reviewed publications and interest in translational (NIH/DoD) research.

What We Offer

●    A fully funded Graduate Research Assistantship (competitive annual stipend plus tuition support).

●    Access to a modern cloud research environment (MonarchSphere on Google Cloud Platform).

●    Mentorship, co-authorship opportunities, and a clear path toward a strong dissertation.

●    A collaborative, interdisciplinary team working on high-impact translational research.

• To apply, please submit your CV/resume, a brief cover letter describing your research interests and relevant experience, and the names of two references to Ruben M.L. Colunga Biancatelli. Applications will be reviewed on a rolling basis until the position is filled. We encourage candidates from all backgrounds to apply.