Digital Plant Phenotyping & ML Scientist

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

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.