Plant Phenotyping ML Co-op — Deep Learning for Agriculture

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

Benefits offered by this job

Health care

Vision

Dental

Retirement

PTO

Sick leave

Job summary

Bayer Crop Science in Missouri (Chesterfield) seeks a graduate student or Ph.D. candidate to develop and optimize deep learning methods for plant phenotyping using imaging and sensor data.

You will implement ML algorithms, evaluate experiments, and report findings to project teams, contributing to data-driven agricultural research while upholding safety standards and collaborative practices.

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

Deep learning

Machine learning

Data analysis

Education

Master's or Ph.D. in Computer Science/EE or agricultural science with CV/ML focus

Tools

TensorFlow

PyTorch

ResNet

YOLO

R-CNN

DeepLab

GANs

VAEs

Transformers

RGB-D cameras

Job description

Bayer Crop Science in Missouri (Chesterfield) seeks a graduate student or Ph.D. candidate to develop and optimize deep learning methods for plant phenotyping using imaging and sensor data.

You will implement ML algorithms, evaluate experiments, and report findings to project teams, contributing to data-driven agricultural research while upholding safety standards and collaborative practices.