USDA-ARS Computational Biology & Machine Learning

About the Role

A research opportunity is available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Genomics and Bioinformatics Research Unit in Gainesville, Florida. 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.

Key Responsibilities

Under the guidance of a mentor, you will:

• Learn how viral evolution influences the reliability of PCR-based diagnostics for avian influenza and other rapidly changing pathogens, 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, 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.

Requirements & Tech Stack

• Programming Languages: Python, Rust (opportunity to learn)
• Tools & Libraries: AlphaFold-based modeling, glycan-aware docking, molecular-dynamics simulations (OpenMM, GROMACS, AMBER force fields), PyTorch, TensorFlow, git, workflow managers (Nextflow), containerization, slurm.
• Methodologies: Machine learning, PCR/qPCR assay design, primer design, molecular diagnostics, protein structure prediction, molecular dynamics, collaborative software development, code review, AI-assisted coding, reproducible research.

Qualifications

• Currently pursuing or have received a doctoral degree in 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.
• U.S. Citizen Only.
• Highly Desired:
• A strong biology/molecular background and the motivation to build real skill in machine learning and computational methods.
• A strong computational/ML background and interest in learning virology and molecular biology.
• Preferred Skills:
• 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).
• Proficiency in Python and scripting.
• Experience with reproducible-research tooling: git, workflow managers (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.

Stipend

$74,677.00 – $74,678.00 Yearly

Appointment Details

• Anticipated Start Date: January 2027 (flexible).
• Appointment Length: Two years.
• Level of Participation: Full-time.
• Proof of health insurance is required for participation.