Computer Vision & Digital Pheno Co-Op

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role are to:

• Analyze and interpret imagery and metrics generated from field trial data using advanced machine learning and computer vision techniques;
• Support experimental design and conduct agronomic assessments using UAV, remote sensing, and manual methods;
• Collaborate closely with field testing and data science partners to integrate digital phenotyping insights;
• Contribute to system and workflow improvements by applying creativity and problem-solving to large-scale data challenges.

• Active MS or PhD candidate in a relevant discipline (data science, precision agriculture, agricultural engineering, plant physiology, crop science, entomology, weed science, or pathology);
• Familiarity with agricultural research and field trial methodologies;
• Proficiency in Python and experience with deep learning frameworks such as PyTorch, TensorFlow, or JAX;
• Solid understanding of core ML concepts (loss functions, regularization, optimization methods such as SGD/Adam, and learning rate scheduling);
• Basic knowledge of remote sensing technologies’
• Ability to work collaboratively across areas of expertise’
• FAA Part 107 license to operate UAVs, or ability to obtain before the start date.

Preferred Qualifications:

• Hands-on experience with modern architectures such as ResNet, UNet, DeepLab, YOLO, SegFormer, SAM, and Vision Transformers;
• Experience with creating and managing large-scale datasets, including the use of tools like Hugging Face Datasets;
• 1–2 years of UAV imaging experience;
• Strong analytical and problem-solving skills;
• Experience with cloud platforms (AWS, GCP, or Azure).

Employees can expect to be paid a salary of approximately between $36.80 to $46.30. Additional compensation may include a bonus or commission (if relevant). Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc.. 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.