AI-Driven Phenomics Postdoc: Predicting Crop Yield

Vylor Inc.; Johnston (IA); On-site; USD 90,000 - 130,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Job summary

Vylor Inc. seeks a highly motivated post‑doctoral scientist to advance AI-enabled plant breeding through multimodal phenomics and predictive modeling.

You’ll work at the intersection of plant breeding, AI, computer vision, and data science to develop novel features, foundation models, and scalable pipelines using diverse sensor data and genomic information. The role focuses on predicting corn yield and breeding outcomes with large-scale phenomics datasets.

Qualifications

• PhD in a related field is required or strongly preferred.
• Strong background in ML/AI for prediction and modeling.
• Experience with multimodal data and phenomics is a plus.

Responsibilities

• Collaborate on a scientific program focused on multimodal AI-driven prediction tasks.
• Develop phenomic features from multimodal sensor data (RGB, LiDAR, hyperspectral, etc.).
• Design and train transformer-based and foundation models for agricultural prediction.
• Identify latent phenotypes and assess their breeding value and environment interactions.
• Build scalable pipelines for large-scale phenotyping data across years/locations.
• Collaborate with breeders, data scientists, and software engineers to translate research.
• Quantify model performance and communicate results to stakeholders.
• Publish findings and contribute to IP and production-ready solutions.

Skills

ML/AI modeling

Computer vision

Multimodal learning

Data analytics

Education

PhD in a related field

Tools

PyTorch

TensorFlow

JAX

HuggingFace

Job description

Vylor Inc. seeks a highly motivated post‑doctoral scientist to advance AI-enabled plant breeding through multimodal phenomics and predictive modeling.

You’ll work at the intersection of plant breeding, AI, computer vision, and data science to develop novel features, foundation models, and scalable pipelines using diverse sensor data and genomic information. The role focuses on predicting corn yield and breeding outcomes with large-scale phenomics datasets.