Plant Health Digital Phenomics Intern

Plant Health Digital Phenomics Intern 

 

 

 

 

 

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;