Postdoctoral Research Associate in AI/Machine Learning

LINCOLN UNIVERSITY • Jefferson City, MO

6d • $0 - $50,000 • Onsite

About The Position

The Postdoctoral Research Associate in AI/Machine Learning (AI/ML) will lead advanced analytical, AI/ML, and data science components of a USDA-funded forest farming project. The position will develop and apply state-of-the-art AI/ML methods to survey data to improve understanding of farmer behavior, forest farming adoption, and network development, and will contribute to capacity-building, and outreach activities that strengthen data-driven forest farming and agroforestry decision-making at Lincoln University and across Missouri.

Requirements

• Ph.D. in Statistics, Data Science, Agricultural or Environmental Data Science, Quantitative Social Science, or a closely related field.
• Experience in developing and applying machine learning models (such as random forests, support vector machines, and neural networks) to empirical datasets.
• Experience with handling survey or social science data (e.g., Likert scales, categorical responses, mixed methods) and performing statistical modeling or ML-based analysis.
• Demonstrated competence in at least one major programming language used for data science (R or Python) and in statistical/ML software workflows.
• Record of peer-reviewed publications.
• Eligibility to work in the United States for the duration of the appointment.

Nice To Haves

• Prior research experience in agriculture, forestry, agroforestry, environmental science, or related fields, especially projects involving farmers or landowners.
• Experience applying AI/ML methods to survey data for nonresponse adjustment, propensity weighting, or behavioral prediction.
• Background in NLP or text analytics, including work with open-ended survey responses, interviews, or focus group transcripts.
• Experience designing or delivering educational content on AI/ML, data science, or cloud computing (e.g., short courses, workshops, online modules) for non-coding or mixed-expertise audiences.
• Familiarity with cloud computing platforms (Google Cloud, AWS) for data analysis and ML model deployment, including use of no-code or low-code tools and AutoML services.
• Demonstrated interest or experience in community-engaged scholarship, extension, or citizen science, especially with underserved or socially disadvantaged farming populations.

Responsibilities

• Design, implement, and validate machine learning models (random forests, support vector machines, neural networks) for survey nonresponse, response propensity estimation, and weighting adjustments using statewide farmer, landowner, and stakeholder survey data.
• Build AI/Natural Language Processing (NLP) pipelines (including large language models) to analyze open-ended survey and focus group data and predict farmer knowledge, attitudes, and adoption willingness.
• Apply multivariate and dimensionality-reduction techniques (PCA, Kernel-PCA, feature selection) to complex, mixed-type datasets.
• Integrate quantitative survey data (Likert-scale, categorical, and continuous variables) with qualitative and text-derived features to build predictive models of forest farming adoption, perceived barriers, and support needs among socially disadvantaged and resource-limited farmers.
• Translate analytical findings into outreach strategies, educational materials, economic analysis inputs, and policy recommendations.
• Prepare technical documentation, reproducible pipelines, and interpretation reports for technical and non-technical audiences.
• Develop and deliver educational modules, short courses, and training materials on AI/ML, data science, and cloud-based analytics (Google Cloud, no-code ML tools) for non-coding students, extension professionals, and farmers, with a strong emphasis on agriculture- and agroforestry-relevant applications.
• Contribute to evaluation metrics and grant deliverables (reports, model portfolios, AI/NLP frameworks, outreach summaries, policy documents).
• Lead or co-author peer-reviewed papers, conference presentations, and extension publications.
• Mentor graduate and undergraduate students on survey, qualitative, and data science tasks.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree