AI- Protein Design and Enzyme Engineering Postdoctoral Associate

AI-Based Protein Design and Enzyme Engineering Postdoctoral Associate

Position Information

Recruitment/Posting Title - AI-Based Protein Design and Enzyme Engineering Postdoctoral Associate

Department
- Quantitative Biomedicine Inst

Salary Details - 63,968 minimum, but commensurate with experience

Offer Information
- The final salary offer may be determined by several factors, including, but not limited to, the candidate's qualifications, experience, and expertise, and availability of department or grant funds to support the position. We also take into consideration market benchmarks, if and when appropriate, and internal equity to ensure fair compensation relative to the university's broader compensation structure. We are committed to offering competitive and flexible compensation packages to attract and retain top talent.

Benefits
- Rutgers provides a comprehensive benefits package to eligible employees. The specific benefits vary based on the position and may include:
Medical, prescription drug, and dental coverage Paid vacation, holidays, and various leave programs Competitive retirement benefits, including defined contribution plans and voluntary tax-deferred savings options Employee and dependent educational benefits (when applicable) Life insurance coverage Employee discount programs

Posting Summary
- The Khare Laboratory at Rutgers University invites applications for a postdoctoral fellow position in computational enzyme engineering and AI-based protein design. A primary project will focus on engineering DNA polymerases supported by a federally funded and multi-institutional collaborative effort on DNA polymerase engineering. Postdoctoral fellows are also encouraged and supported to develop independent research directions aligned with the lab's scientific program.

The laboratory develops and applies computational and machine-learning methods for enzyme engineering, de novo protein design, and the design of protein function. Research is conducted in close collaboration with experimental colleagues within and outside the group at Rutgers and beyond, with iterative cycles between computational design and experimental characterization.

Position Status
- Full Time

Posting Number - 26FA0469

Posting Open Date - 05/08/2026

Posting Close Date - 07/31/2026

Qualifications

Minimum Education and Experience
- • PhD (awarded or expected within six months of start date) in computational biology, bioinformatics, biophysics, chemistry, computer science, or a closely related field.

Certifications/Licenses

Required Knowledge, Skills, and Abilities
- • Publication record (published, in press, or as a preprint) in protein design, protein engineering, computational structural biology, or machine learning for biology.
• Demonstrated programming proficiency in Python, including experience with modern deep-learning frameworks (PyTorch and/or JAX).
• Submission of a representative code sample or link to a public code repository (e.g., Git Hub) as part of the application.

Preferred Qualifications
- • Direct experience with contemporary protein design tools and models (e.g., Rosetta, ProteinMPNN, RF diffusion, Alpha Fold-family models, ESM-based or other protein language models, or comparable methods).
• Experience training or fine-tuning ML models on large experimental datasets.
• Prior track record of close collaboration with experimentalists.

Equipment Utilized

Physical Demands and Work Environment

Overview
- • Design, implement, and apply AI-based methods merged with molecular modeling for protein design and engineering, including one or more of de novo design, sequence (re) design, and design of functionally relevant properties such as substrate selectivity and catalytic activity, stability, enzyme complementation, and photo control.
• Develop and train ML models informed by diverse experimental data such as (a) enzyme activity, specificity, and stability measurements; and (b) high-throughput sequencing data based on library screening and/or directed evolution.
• Collaborate closely with experimentalists on design–build–test–learn cycles including analysis and design of libraries.
• Prepare and publish manuscripts; present at conferences; contribute to grant-related reporting.
• Mentor graduate and undergraduate trainees.