Digital Plant Phenotyping & Machine Learning Co-Op

Bayer AG; Chesterfield (MO); On-site; USD 31,000 - 66,000; Full time

Job summary

Bayer AG is seeking a candidate pursuing a Master’s or Ph.D. in CS/EE or agricultural science with a focus on computer vision or ML to develop and optimize deep learning methods for plant phenotyping. You will work with imaging and sensor data to derive actionable insights and support data-driven decisions.

The role emphasizes collaboration across teams, experimental design, and reporting results. Familiarity with AWS or cloud deployment is a plus, and safety/compliance are prioritized

Qualifications

• Enrollment in a master’s or Ph.D. program in Computer Science, Electrical Engineering, or an agricultural science program with a focus on computer vision or machine learning.
• Solid foundation in Python programming and familiarity with deep learning frameworks such as TensorFlow or PyTorch.
• Experience with model architectures and tools including ResNet, YOLO, R-CNN, DeepLab, GANs, VAEs, and Transformers.
• Experience with hardware and sensing platforms such as RGB-D cameras, LiDAR sensors, robotics, and other imaging or phenotyping systems.

Responsibilities

• Implement and optimize deep learning and machine learning algorithms, leveraging generative models for actionable insights and solutions.
• Evaluate needs, recommend experiments and projects, advocate for novel algorithmic pursuits, and inform strategic decisions.
• Utilize imaging and sensor technologies to collect and analyze phenotypic data, such as plant growth, plant development, and biotic and abiotic responses.
• Communicate results in a timely and organized fashion to project teams and key stakeholders through scientific reports and presentations.
• Solve complex problems autonomously requiring original thinking, creativity, and deductive reasoning, and apply scientific principles to the design and interpretation of scientific experiments.
• Perform multiple experimental protocols under supervision, as needed.
• Prioritize and coordinate work within a matrixed testing environment while maintaining detailed record keeping and required documentation.
• Demonstrate strong commitment to safety and compliance by adhering to safety protocols and best practices.

Skills

Python

TensorFlow

PyTorch

ResNet

YOLO

R-CNN

DeepLab

GANs

VAEs

Transformers

RGB-D cameras

LiDAR

Robotics

Education

Enrollment in a master’s or Ph.D. program in Computer Science, Electrical Engineering, or agricultural science with focus on computer vision or machine learning

Tools

TensorFlow

PyTorch

Job description

Digital Plant Phenotyping & Machine Lear

In this role, you will implement and optimize advanced deep learning and machine learning approaches to generate actionable insights from imaging and sensor data, supporting data-driven decision making in plant phenotyping and agricultural research.

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role are to:

• Implement and optimize deep learning and machine learning algorithms, leveraging generative models for actionable insights and solutions;
• Evaluate needs, recommend experiments and projects, advocate for novel algorithmic pursuits, and inform strategic decisions;
• Utilize imaging and sensor technologies to collect and analyze phenotypic data, such as plant growth, plant development, and biotic and abiotic responses;
• Communicate results in a timely and organized fashion to project teams and key stakeholders through scientific reports and presentations;
• Solve complex problems autonomously requiring original thinking, creativity, and deductive reasoning, and apply scientific principles to the design and interpretation of scientific experiments;
• Perform multiple experimental protocols under supervision, as needed;
• Prioritize and coordinate work within a matrixed testing environment while maintaining detailed record keeping and required documentation;
• Demonstrate strong commitment to safety and compliance by adhering to safety protocols and best practices.

WHO YOU ARE

Bayer seeks an incumbent who possesses the following:

Required Qualifications:

• Enrollment in a master’s or Ph.D. program in Computer Science, Electrical Engineering, or an agricultural science program with a focus on computer vision or machine learning;
• Solid foundation in Python programming and familiarity with deep learning frameworks such as TensorFlow or PyTorch;
• Experience with model architectures and tools including ResNet, YOLO, R-CNN, DeepLab, GANs, VAEs, and Transformers;
• Experience with hardware and sensing platforms such as RGB-D cameras, LiDAR sensors, robotics, and other imaging or phenotyping systems.

Preferred Qualifications:

• Previous experience with cloud platforms for model deployment, including AWS, Google Cloud, or Azure;
• Experience using computer modeling techniques for plant development and image-based plant phenotyping;
• Experience implementing machine learning and statistical models to identify or evaluate biotic and/or abiotic stresses in plants.

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

YOUR APPLICATION

Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Health for all, Hunger for none,
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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