Postdoctoral Associate in Virus-Immune System Coevolution

University of Florida • Gainesville, FL

10h

About The Position

Dr. Yiquan Wang ’s laboratory ( Lab Website ) in the department of Infectious Diseases and Immunology at the University of Florida is recruiting postdoctoral researchers. The group integrates artificial intelligence, structural biology, and high-throughput experimental techniques to study sequence–structure–function relationships that shape viral evolution and immune recognition. By combining deep mutational scanning, next-generation sequencing, and foundation models, we develop interpretable AI to anticipate viral evolution and enable next-generation therapeutics and vaccines. Research projects will involve engineering high-throughput library-on-library screening platforms for viral immunology, building AI foundation models to predict viral antigenic evolution and design effective antibody, developing interpretable multimodal AI frameworks linking sequence, structure, and function, and designing autonomous AI agents for automated experimentation and data analysis. Candidates should possess a strong collaborative mindset and enthusiasm for interdisciplinary research. We are seeking experimental candidates with experience in high-throughput techniques such as yeast display and deep mutational scanning, or computational candidates with experience in generative AI, reinforcement learning, or agentic AI. The lab is supported by world-class infrastructure, providing trainees with access to UF’s HiPerGator supercomputing facility, including 50 NVIDIA B200 GPUs, and a high-throughput automated screening platform. We offer a supportive, collaborative training environment with close mentorship and strong support for publications, conferences, internships, and long-term academic or industry careers.

Requirements

• Ph.D. or equivalent degree in computer science, bioengineering, biological sciences, bioinformatics, computational biology, or a closely related field.

Nice To Haves

• Strong publication record in peer-reviewed journals or top-tier conferences relevant to machine learning, computational biology, virology, immunology, or structural biology.
• Demonstrated experience with machine learning or deep learning methods, particularly for biological sequence, structure, or multimodal data (Computational Candidates).
• Hands-on experience with one or more experimental platforms, such as molecular cloning, viral reverse genetics, cell culture, protein expression/purification, antibody characterization, or high-throughput screening (Experimental Candidates).
• Ability to work collaboratively across computational and experimental domains, with strong communication skills.
• Motivation to pursue independent, creative research at the interface of AI and biology.

What This Job Offers

• Job Type: Full-time
• Education Level: Ph.D. or professional degree