TES Research Associate

Overview

The TES Research Associate will work with scientists and students under the supervision of Dr. Chen - to support research related to AI-driven modeling of forestry systems, plant physiological processes, and ecological traits. The position will focus on developing computational models integrating multi-source datasets, including remote sensing, field measurements, environmental variables, and biological trait data.

Temporary Employment Services (TES), a unit of the Auburn University Department of Human Resources, is an in-house support center established to meet the temporary employment needs of the university. TES provides qualified and dedicated temporary employees in a wide variety of occupations to meet the staffing needs throughout the campus. Temporary employees are hired for a variety of reasons with the most common being:

Assistance in the place of a regular employee who is absent for a specified period of time

Additional assistance during periods of abnormal or peak workloads

Assistance with special projects

Seasonal work

Emergencies

If you are looking for an employment opportunity, TES is a great way to showcase your professional skills and assist Auburn University while gaining valuable work experience within higher education.

AU students are not eligible for TES.

Responsibilities

Developing and implementing machine learning and deep learning models to analyze forestry, physiological, and ecological datasets

Modeling plant growth, carbon allocation, stress response (e.g., drought, salinity), and ecosystem productivity using AI-based approaches

Integrating multi-modal datasets (remote sensing, UAV /drone imagery, field inventory data, soil and climate parameters, and genomic/trait data)

Conducting statistical modeling, feature selection, and predictive analytics for forest health, resilience, and biomass estimation

Supporting data preprocessing, cleaning, normalization, and database organization

Assisting in the preparation of research reports, manuscripts, and grant proposals

This position will provide hands-on experience in applying artificial intelligence and computational modeling techniques to complex biological and ecological systems, with applications in forest productivity, climate resilience, and sustainable resource management.

Qualifications

Master's degree in Computer Science, Artificial Intelligence, Data Science, or a closely related field.

Minimum Knowledge, Skills, and Abilities

Experience in machine learning, deep learning, and statistical modeling

Strong programming skills (e.g., Python, R, TensorFlow/PyTorch)

Experience handling large-scale ecological, biological, or remote sensing datasets

Strong scientific writing and communication skills

Interest in interdisciplinary research in forestry, plant physiology, and ecosystem science

Why Work at Auburn?

Life-Changing Impact: Our work changes lives through research, instruction, and outreach, making a lasting impact on our students, our communities, and the world.

Culture of Excellence: We are committed to leveraging our strengths, resources, collaboration, and innovation as a top employer in higher education.

We’re Here for You: Auburn offers generous benefits, educational opportunities, and a culture of support and work/life balance.

Sweet Home Alabama: The Auburn/Opelika area offers southern charm, vibrant downtown scenes, top-ranked schools, and easy access to Atlanta, Birmingham, and the Gulf of Mexico beaches.

A Place for Everyone: Auburn is committed to fostering an environment where all faculty, staff, and students are welcomed, valued, respected, and engaged.

Ready to lead and shape the future of higher education? Apply today! War Eagle!

Developing and implementing machine learning and deep learning models to analyze forestry, physiological, and ecological datasets

Modeling plant growth, carbon allocation, stress response (e.g., drought, salinity), and ecosystem productivity using AI-based approaches

Integrating multi-modal datasets (remote sensing, UAV /drone imagery, field inventory data, soil and climate parameters, and genomic/trait data)

Conducting statistical modeling, feature selection, and predictive analytics for forest health, resilience, and biomass estimation

Supporting data preprocessing, cleaning, normalization, and database organization

Assisting in the preparation of research reports, manuscripts, and grant proposals

This position will provide hands-on experience in applying artificial intelligence and computational modeling techniques to complex biological and ecological systems, with applications in forest productivity, climate resilience, and sustainable resource management.

Master's degree in Computer Science, Artificial Intelligence, Data Science, or a closely related field.