USDA-ARS Computational Biology & Machine Learning for Avian Influenza Surveillance
Oak Ridge Associated Universities
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Job summary
USDA ARS Genomics and Bioinformatics Research Unit in Gainesville, FL seeks a doctoral candidate to work on PCR-based diagnostics, sequencing assays, and structural modeling related to avian influenza and mammalian adaptation. The two-year, full-time fellowship emphasizes ML, data analysis, and collaborative software development.
You will work with AlphaFold-based modeling, glycan-aware docking, and MD simulations, with a mentor guiding balance between projects based on strengths.
Qualifications
- PhD in biology, molecular biology, bioinformatics, computational biology, ML, biophysics, or related field.
- Degree must be recent or expected by 1/30/2027.
Responsibilities
- Develop ML models predicting primer-template mismatches and assay performance.
Skills
Education
Tools
Job description
U.S. Department of Agriculture (USDA) - ARS Genomics and Bioinformatics Research Unit, located in Gainesville, Florida.
USDA-ARS-HQPD-2026-0428
1/15/2027 3:00:00 PM Eastern Time Zone
Applications are reviewed on a rolling-basis and may close before the deadline.
The H5N1 outbreak in U.S. dairy cattle showed that influenza can establish itself in mammalian hosts and that our surveillance tools must keep pace with a changing virus. This fellowship combines two efforts: making PCR-based diagnostics and sequencing assays robust to viral mutation, and tracking viral adaptation to mammals through structural modeling. You will have the freedom to set the balance between them with your mentor, based on your strengths and early results.
With your mentor, you will learn how to build machine-learning models that predict how primer-template mismatches reduce amplification and extension efficiency in avian influenza assays, so likely failures can be flagged before they affect testing. You will use a large molecular training dataset and validate against new data from federal partner labs at the National Poultry Research Center and National Animal Disease Center. This is one of a set of foundational PCR modeling projects pairing optimization and ML methods for multiplex primer design: our group recently published PRISM (Chen et al., Bioinformatics, 2026), a submodular-optimization framework that designs multiplex primers. A central question is how to bring mismatch-aware predictions into this design framework. This learning experience is a collaboration with Dr. David Suarez, who developed the key avian influenza diagnostic tests.
With your mentor, you will learn how to develop structure-based approaches to estimate how hemagglutinin and neuraminidase variants interact with avian-type (a2,3) and human-type (a2,6) sialic acid receptors, toward scalable monitoring of mammalian adaptation. This is a new direction for the lab, and we are still identifying potential structural-biology collaborators. Methods may include AlphaFold-based modeling, glycan-aware docking or co-folding, and OpenMM with AMBER-family force fields.
- Learn how viral evolution influences the reliability of PCR-based diagnostics for avian influenza and other rapidly changing pathogens while developing computational primer-design and optimization approaches for multiplex PCR assays.
- Develop skills in machine learning, molecular-data analysis, and structural biology by modeling primer-template mismatches, studying influenza protein adaptation to mammalian receptors, and using tools such as AlphaFold-based protein modeling, glycan-aware docking, and molecular-dynamics simulations.
- Gain experience validating computational predictions with laboratory-generated diagnostic data from federal animal-health research partners while building an understanding of influenza virology, molecular diagnostics, viral host adaptation, and zoonotic-disease surveillance.
- Strengthen programming and research-computing skills in Python, with opportunities to learn Rust, collaborative software development, code review, AI-assisted coding practices, and the development of reproducible open-source research tools and datasets.
- Build interdisciplinary research, scientific communication, and professional skills through collaboration across biology, virology, bioinformatics, machine learning, and structural modeling, as well as through conference presentations and first-author publication opportunities.
The mentor for this opportunity is Adam Rivers (adam.rivers@usda.gov). Dr. Adam Rivers is a coauthor of QIIME 2, a widely used bioinformatics platform, and developer of tools including ITSxpress, Guidemaker, Branchwater, and Tamipami. The lab builds open, reproducible software that other scientists adopt. If you have questions about the nature of the research, please contact the mentor.
Anticipated Appointment Start Date: January 2027. Start date is flexible and will depend on a variety of factors.
Appointment Length: The appointment will be for two years.
Level of Participation: The appointment is full time.
Citizenship Requirements: This opportunity is available to U.S. citizens only.
$74,677.00 - $74,678.00 Yearly
Janeen
The qualified candidate should be currently pursuing or have received a doctoral degree in the one of the relevant fields (biology, molecular biology, bioinformatics, computational biology, machine learning, biophysics, or a related field). Degree must have been received within the past four years, or anticipated to be received by 1/30/2027.
- A strong biology/molecular background and the motivation to build real skill in machine learning and computational methods (we’ll train you), or;
- A strong computational/ML background and interest in learning the virology and molecular biology (we’ll train you on that side).
- Experience with PCR/qPCR assay design, primer design, or molecular diagnostics
- Familiarity with protein structure prediction tools (AlphaFold, ESMFold) or molecular dynamics packages (OpenMM, GROMACS, AMBER force fields)
- Hands-on deep learning experience (PyTorch or TensorFlow), even outside biological data
- Proficiency in Python and scripting ; willingness to pick up Rust as the lab transitions
- Experience with reproducible-research tooling: git, workflow managers like Nextflow, containerization, slurm
- Background in virology, influenza biology, microbiology or animal/agricultural disease systems
- A track record of first-author publication or open-source software contribution
- Comfortable researching across a disciplinary boundary, either explaining computational methods to biologists or biological context to computational collaborator
- Citizenship: U.S. Citizen Only
- Degree: Doctoral Degree received within the last 48 months or anticipated to be received by 1/30/2027 12:00:00 AM.
- Discipline(s):
- Chemistry and Materials Sciences
- Analytical Chemistry
- Bio-inorganic Chemistry
- Bio-organic Chemistry
- Biophysical Chemistry
- Chemistry (General)
- Environmental Chemistry
- Inorganic Chemistry
- Materials Sciences
- Organic Chemistry
- Physical Chemistry
- Polymer Chemistry
- Theoretical Chemistry
- Computer, Information, and Data Sciences
- Earth and Geosciences
- Engineering
- Environmental and Marine Sciences
- Life Health and Medical Sciences
- Mathematics and Statistics
- Physics
- Science & Engineering-related
- Veteran Status: Veterans Preference, degree received within the last 120 month(s).
- Chemistry and Materials Sciences
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