Postdoctoral Researcher in Statistical Phylogenetics

Company Description The University of Hawaiʻi at Mānoa, founded in 1907, is the flagship campus of the University of Hawaiʻi System and a destination for students and scholars from around the world. The university offers distinctive research opportunities, a diverse and inclusive community, and a nationally ranked Division I athletics program. Located in a unique Hawaiian setting, UH Mānoa provides a multicultural, global learning environment that supports interdisciplinary collaboration.

Role Description The Postdoctoral Researcher in Statistical Phylogenetics will conduct original research on the development and application of statistical methods for evolutionary inference, including modeling, simulation, and analysis of phylogenetic data. Day-to-day activities include designing and implementing computational and statistical workflows, analyzing large biological datasets, contributing to methodological advancements, and collaborating with faculty, students, and external partners. The researcher will prepare manuscripts for publication, present findings at seminars and conferences, and assist with grant-related activities. Limited mentoring or teaching of graduate and undergraduate students, such as guiding research projects or contributing to specialized courses, may be expected. This is a full-time, on-site role based in Honolulu, HI.

Qualifications

• Strong research skills in statistical phylogenetics, including experience formulating hypotheses, designing studies, and publishing in peer-reviewed journals.
• Proficiency in data analysis and quantitative methods, including statistical modeling, computational workflows, and programming in languages such as C/C , Rust, Python, or similar tools.
• Relevant background in physics, mathematics, or related quantitative disciplines that supports rigorous methodological development and problem-solving.
• Familiarity with molecular and evolutionary biology workflows that inform phylogenetic data generation and interpretation.
• Teaching or mentoring experience, including the ability to support students in research methods, data analysis, and scientific communication.
• Doctoral degree (Ph.D.) in statistics, applied math, physics, or a closely related field by the start date of the appointment.
• Strong written and verbal communication skills, with the ability to present complex concepts clearly to diverse audiences.
• Demonstrated ability to work collaboratively in interdisciplinary teams and manage research projects independently.
• Experience with high-performance computing environments and version control (e.g., Git) is highly beneficial.