Production Research Field Testing Intern

Bayer (Schweiz) AG; Chesterfield, Northern (MO, KY); Hybrid; USD 30,000 - 66,000; Full time

Job summary

Bayer in Chesterfield, MO invites a graduate student to contribute to Plant Health Digital Phenomics during a 3-month internship. You will build and validate AI-driven phenomics pipelines, convert image data into quantitative traits, and help inform breeding decisions in row crops.

This role blends plant science with digital analytics in a hands-on environment. You will work alongside scientists in controlled-environment phenotyping, develop image analysis tools, and learn to translate manual

Qualifications

• Pursuing a graduate degree (MS or Ph.D.) in plant science or computational discipline.
• Experience in controlled-environment research and plant disease digital phenotyping.
• Experience with data analysis and programming (Python, R).
• Familiarity with computer vision, image analysis, or ML concepts.
• Willingness to relocate to Chesterfield, MO for a 3-month internship.

Responsibilities

• Develop and validate automated image analysis pipelines (AI/ML) to extract disease and phenotypic traits.
• Work alongside scientists in controlled-environment phenotyping and data analysis.
• Solve technical and operational challenges independently to complete experiments on time.
• Adhere to safety protocols in greenhouse, growth chamber, and lab environments.

Skills

Plant science

Computational discipline

Controlled-environment research

Data analysis

Python or R

Computer vision

Self-motivation

Relocation willingness

Education

MS/PhD candidate

Tools

OpenCV

YOLO

SAM

Job description

Plant Health Digital Phenomics Intern

Chesterfield, Missouri, United States

ID: 884370

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

The primary responsibilities of this role include:

• 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

Bayer seeks an incumbent who possesses the following:

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

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.

Equal Opportunity Employer Statement: Notice for U.S. Visitors: All information on this site is subject to compliance with local rule and regulations as they may vary from time to time and across different geographies, including, without limitation, U.S. Executive Orders.

Bayer is an E-Verify Employer.

Location: United States : Missouri : Chesterfield

Division: Crop Science

Reference Code: 884370

Contact Us

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