Digital Genomics Prediction Intern

At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. We invite you to help shape the future of agriculture and play a role in safely feeding the world while caring for our planet.
Syngenta's Analytics & Data Sciences team is seeking a Digital Genomics Prediction Intern in Durham, NC. This internship provides practical experience applicable to careers in agricultural technology, plant sciences, or graduate research programs. You'll gain exposure to industry-leading phenotyping infrastructure and contribute meaningful insights to active breeding programs.
As part of Syngenta's Emerging Talent Program, you will gain hands-on experience, exposure to day-to-day business operations, and opportunities to apply your academic knowledge to real-world challenges while developing your professional skills.
Duration: May 2027 – August 2027
Accountabilities
Support phenotyping platform operations such as sensor deployment, image acquisition, and field data collection across greenhouse and field environments.
Contribute to image processing workflows, trait extraction algorithms, and analytical pipeline optimization using advanced computational tools.
Lead an independent research initiative focused on phenotyping methodology validation or trait measurement innovation, culminating in a presentation to research leadership.
Partner with plant breeders, agronomists, and data scientists to integrate phenotypic insights into breeding decisions and product development.
Maintain detailed experimental records, standard operating procedures, and technical documentation to ensure reproducibility.
Required
Currently enrolled as a graduate student in an accredited college/university.
Pursuing degree in Plant Breeding, Plant Science, Agronomy, Computer Science, Data Science, Engineering, Biology, Plant Pathology, Horticulture, Entomology, Weed science Crop Science, or related discipline.
Hands-on experience with agricultural research, remote sensing, field trials, or crop production systems beneficial.
Programming proficiency in Python and/or R for data analysis and visualization.
Minimum 3.0 cumulative GPA.
Strong analytical thinking and problem-solving abilities.
Effective communication skills for technical and non-technical audiences.
Desired
Experience working with geospatial datasets.
Understanding of GIS software (ArcGIS Pro, QGIS) and spatial databases.
Experience with machine learning applications in remote sensing (classification, regression, object detection).

Employment type
Intern

Work arrangement
Yes