Postdoctoral Fellow: Evolutionary Biology & Protein AI

Rutgers University; Piscataway Township (NJ); On-site; USD 64,000 - 76,000; Full time

Benefits offered by this job

Medical coverage

Dental coverage

Retirement benefits

Educational benefits

Life insurance

Employee discounts

Job summary

Rutgers University’s Kaelber lab in the Department of Molecular Biology and Biochemistry on the Busch Campus seeks a Post Doctoral Associate to develop and benchmark evolutionary analysis methods, including protein structure comparisons and AI approaches.

The position involves collaboration with wet-lab experiments, mentoring undergraduates, and contributing to origins of life research with Bayesian reconstructions and CNNs, with a focus on rigorous validation and publication-ready results.

Qualifications

• Ph.D. in life sciences, chemical sciences or other related field.
• Excellence in at least two of the following are required; structural biology, programming and high-performance computing, bioinformatics or evolutionary theory.
• Required skills include commitment to ethical conduct of research, self-motivation and independence, good documentation habits and a track record of productive scientific results.

Responsibilities

• Develop and benchmark methods for evolutionary analysis by comparing protein structures.
• Conduct rigorous evaluations of concordance of computational results to validation sets.
• Collaborate with wet lab experimentalists and interdisciplinary researchers; mentorship of undergraduates may be encouraged.

Skills

Structural biology

Programming

Bioinformatics

Evolutionary theory

Education

Ph.D. in life sciences/related field

Tools

High-performance computing

Job description

Rutgers University’s Kaelber lab in the Department of Molecular Biology and Biochemistry on the Busch Campus seeks a Post Doctoral Associate to develop and benchmark evolutionary analysis methods, including protein structure comparisons and AI approaches.

The position involves collaboration with wet-lab experiments, mentoring undergraduates, and contributing to origins of life research with Bayesian reconstructions and CNNs, with a focus on rigorous validation and publication-ready results.