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Job 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.
The internship is located in Chesterfield, MO and is 100% in‑person. The intern will have the opportunity to increase business acumen and professional development through in‑person and virtual networking opportunities throughout the term.
Your Tasks And 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.
Who You Are
Required Qualifications:
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 (as specified in the full job description).
Employees can expect to be paid a salary of approximately between $22.75 to $47.75. Additional compensation may include a bonus or commission (if relevant). Additional benefits may include health care, vision, dental, retirement, PTO, sick leave, etc (if relevant). This salary (or salary range) is merely an estimate and may vary based on an applicant’s location, market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors.
This posting will be available for application until at least March 19, 2027
To all recruitment agencies: Bayer does not accept unsolicited third party resumes.
Bayer is an Equal Opportunity Employer/Disabled/Veterans
Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below.
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Bayer is an E-Verify Employer.
Location: United States : Missouri : Chesterfield
Division: Crop Science
Reference Code:
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Email:
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Worksite address
chesterfield, MO, 63005, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.