Digital Plant Phenotyping & Machine Learning Co-Op

Bayer CropScience Limited; Chesterfield (MO); On-site; USD 32,000 - 66,000; Full time

Benefits offered by this job

Health care

Retirement plan

Paid time off

Job summary

Bayer CropScience in Missouri is seeking a researcher in digital plant phenotyping to develop and optimize deep learning methods for phenotypic analysis. You will leverage generative models and a range of imaging sensors to extract actionable insights for plant development and stress responses.

The role emphasizes autonomous problem solving, rigorous data handling, and collaboration with project teams, with safety and documentation as priorities.

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, development, and responses.
• Communicate results in a timely and organized fashion to project teams and stakeholders through reports and presentations.
• Solve complex problems autonomously requiring original thinking and apply scientific principles to experimental design and interpretation.
• Perform multiple experimental protocols under supervision as needed.
• Prioritize and coordinate work within a matrixed testing environment while maintaining documentation.
• Demonstrate commitment to safety and compliance by adhering to safety protocols.

Skills

Python

TensorFlow

PyTorch

ResNet

YOLO

R-CNN

GANs

VAEs

Transformers

RGB-D cameras

LiDAR

Cloud platforms

Education

Masters/PhD program in Computer Science, Electrical Engineering, or agricultural science with CV/ML focus

Tools

AWS

Google Cloud

Azure

Job description

Digital Plant Phenotyping & Machine Lear

YOUR TASKS AND 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.

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

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: 882876

Email: [e-mail]