Postdoctoral Scholar in Natural Language Processing and Mammalian Evolution

 Ph.D. in natural language processing, computer science, data science, ecology, evolutionary biology, biodiversity informatics, bioinformatics, or a closely related field, completed or expected to be completed before the start date.

Centuries of field observations, dietary studies, and taxonomic accounts contain an extraordinary amount of information about what mammals eat. However, these data remain embedded in unstructured text, scattered across thousands of publications, and are reported using inconsistent terminology and formats. This project seeks to unlock these hidden data by developing a human-in-the-loop AI pipeline that uses LLMs to read scientific literature at scale, extract consumer-food relationships, and convert qualitative descriptions into standardized ordinal dietary rankings suitable for evolutionary biology research.

Our central hypothesis is that the published literature contains a vast reservoir of latent dietary information that can be systematically transformed into reproducible quantitative datasets. Existing mammalian diet databases rely heavily on manual expert curation, limiting their scope and scalability. By combining AI-assisted information extraction with expert validation, this project will create high-resolution dietary datasets spanning the mammalian tree at a scale that would be impractical through manual curation alone.

The Postdoctoral Researcher will play a leading role in developing the dietary ranking framework and constructing the resulting database. Working closely with an interdisciplinary team of ecologists, biodiversity scientists, and AI researchers, the successful candidate will contribute to the development and evaluation of LLM-based information extraction methods, analyze large-scale dietary datasets, and help advance computational approaches for ecological synthesis.

The Postdoctoral Researcher will conduct independent and collaborative research, publish in leading scientific journals, contribute to grant proposals, mentor graduate and undergraduate students, and help shape the future direction of this growing interdisciplinary research program. The project also provides excellent opportunities to develop expertise in AI for biodiversity science while building a strong publication record and collaborative network for an academic or research career.

This position will initially be awarded for one year and, contingent upon strong performance and conduct and the availability of funds, may be renewed for up to three years. 

The University of Florida is an Equal Opportunity institution. Hiring is contingent upon eligibility to work in the United States. Searches are conducted in accordance with Florida’s Sunshine Law.

$60,000

· Demonstrated research experience in at least one of the following areas:

o Natural language processing, large language models (LLMs), information extraction, or machine learning; or

o Ecology, evolutionary biology, biodiversity informatics, or similar, with a focus on large-scale, quantitative research.

· Strong programming skills in Python and experience with scientific computing, data processing, and reproducible research workflows.

· Experience designing, implementing, and evaluating computational methods or data analysis pipelines.

· Excellent written and oral communication skills, with evidence of peer-reviewed publications or a strong publication trajectory.

· Ability to work independently while contributing effectively to an interdisciplinary, collaborative research team.

· Experience with modern NLP and LLM techniques, including transformer models, prompting strategies, named entity recognition, relation extraction, or information extraction from scientific literature.

· Experience working with biodiversity, ecological, evolutionary, or biological trait datasets.

· Familiarity with annotation workflows, corpus development, or evaluation of machine learning models.

· Experience with statistical analysis, multivariate methods, or comparative and phylogenetic analyses.

· Experience using high-performance computing (HPC), cloud computing, or large-scale machine learning workflows.

· Familiarity with open science, FAIR data principles, and research data management.

· Experience mentoring undergraduate or graduate students and contributing to collaborative research projects.

For full consideration, applications must be submitted online. Click on Apply Now at the top of this posting.

Application reviews will begin Dec 15, 2026. Only complete applications will be reviewed at this time.

A complete application includes:

• • • A complete curriculum vitae
    • Letter of intent, summarizing the applicant's qualifications, interests, and suitability for the position, two page maximum.
    • Names and contact information of three professional references

We expect the Postdoctoral Researcher to begin in late 2026 or spring of 2027, with some flexibility in start date.  Initial review of applicants will begin on 15 October 2026. Only complete applications will be reviewed at this time.

This is a time-limited position.