Description
In this 3-month internship, you will contribute to the success of the Breeding Plant Health team by building and validating controlled-environment digital phenomics solutions.
You will bridge plant pathology with digital innovation, applying image analysis, artificial intelligence, and data-driven approaches to transform disease phenotyping from manual visual scoring into high-throughput, automated, quantitative trait extraction that directly informs Bayer's breeding advancement decisions in row crops.
This role is ideal for a technology-savvy plant scientist passionate about combining plant health, agronomy, and quantitative data analysis to solve real-world biological challenges.
Responsibilities:
- Work alongside scientists in controlled-environment phenotyping and digital image/data analysis to support the Breeding Plant Health team across key patho-systems;
- Develop and validate automated image analysis pipelines (AI/ML) to extract disease and phenotypic traits from imagery, benchmarking results against manual scores and genomic prediction indices;
- Solve technical and operational challenges independently, drawing on prior experience and consultation with team members to complete experiments on time;
- Achieve commitment to safety and compliance, adhering to safety protocols and best practices in greenhouse, growth chamber, and lab environments.
Requirements:
- Currently pursuing a graduate degree (M.S. or Ph.D.) in a plant science discipline or a computational discipline (Computer Science, Computer Vision, Data Science, or Biosystems/Agricultural Engineering), with demonstrated interest at the intersection of crop health and digital/AI-driven analysis;
- Experience in controlled-environment research and plant disease digital phenotyping, with interest in applying AI/ML tools to crop health and breeding challenges;
- Experience with data analysis, statistical methods, and programming (Python, R, or similar) for processing and interpreting plant disease phenotyping datasets;
- Familiarity with computer vision, image analysis, or machine learning concepts, including image annotation, object detection, segmentation, or predictive modeling;
- Self-motivated and takes initiative to meet goals with minimal supervision; proven problem-solver;
- Willingness and ability to relocate for the 3-month internship to Chesterfield, MO.
Preferred Qualifications:
- Experience developing image segmentation, object detection, or classification pipelines using tools such as OpenCV, Segment Anything Model (SAM/SAM2), YOLO, or similar frameworks;
- Hands-on experience with image processing, computer vision, or related AI/ML workflows for plant disease or agronomic trait analysis.
Benefits:
- Salary of approximately between $22.75 to $47.75
- Additional compensation may include a bonus or commission
- Additional benefits may include health care, vision, dental, retirement, PTO, sick leave, etc