Postdoctoral Associate

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.

Salary will be commensurate with the applicant’s qualifications and experience.

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

• 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.

Applications for job number 538681 will be considered as they are received, but for full consideration, applicants must upload a PDF file containing a current curriculum vitae, a brief summary of your research experience and interests, and a list of 3 professional references with contact information online and sent electronically via email to Dr. Yiquan Wang at wang.yiquan@ufl.edu. The review of applications will begin immediately and will continue until the position is filled.

The successful candidate will be required to provide official transcript(s) upon hire. A transcript will not be considered "official" if a designation of "Issued to Student" is visible. Degrees earned from an educational institution outside of the United States are required to be evaluated by a professional credentialing service provider approved by the National Association of Credential Evaluation Services (NACES), which can be found at http://www.naces.org/.

The University of Florida is an equal-opportunity institution. If an accommodation due to a disability is needed to apply for this position, please call 352/392-2477 or the Florida Relay System at 800/955-8771 (TDD). Hiring is contingent upon eligibility to work in the US. Searches are conducted in accordance with Florida's Sunshine Law.

Application must be submitted by 11:55 p.m. (ET) of the posting end date.

This is a time-limited position.