Text To Survey Jobs (NOW HIRING)

PURPOSE:

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.

ESSENTIAL JOB FUNCTIONS, DUTIES, & RESPONSIBILITIES:

• List essential job functions, duties & responsibilities for the role. Try to be as detailed as possible.
• 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.

QUALIFICATIONS:

• List mandatory qualifications.
• 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.

PREFERRED QUALIFICATIONS:

• List any preferred / optional qualifications for this role.
• 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.

PHYSICAL DEMANDS:

• List physical demands as necessary. If none, describe the environment the employee will be working in, i.e. This position will work in an office environment with minimal exposure to physical work.
• This position will work primarily in an office environment.
• Occasional travel may be required for project meetings, workshops, field days, or outreach events at Lincoln University facilities, partner institutions, and community sites across Missouri.

This job description is not intended to be a complete list of all responsibilities, duties or skills required for the job and is subject to review and change at any time, with or without notice, in accordance with the needs of Lincoln University. Since no job description can detail all the duties and responsibilities that may be required from time to time in the performance of a job, duties and responsibilities that may be inherent in a job, reasonably required for its performance, or required due to the changing nature of the job shall also be considered part of the jobholder's responsibility.