Postdoctoral Research Associate - Phenotyping and AI

Requisition Id 13218 

Overview: 

Oak Ridge National Laboratory (ORNL) is the largest US Department of Energy science and energy laboratory, conducting basic and applied research to deliver solutions to compelling problems in energy and security. Our capabilities span a broad range of scientific and engineering disciplines, enabling the Laboratory to explore fundamental science challenges and to carry out the research needed to accelerate the delivery of solutions to the marketplace.

 

The Plant Systems Biology (PSB) group of the Biosciences Division (www.ornl.gov/sci/ees/bsd/) at ORNL has an opening for a Postdoctoral Research Associate to conduct research on root and shoot phenomics with applications of AI and deep learning towards better understanding of plant genes and phenotypes that are foundational to developing sustainable biomass feedstocks. The selected applicant will join a team of diverse, cross-disciplinary scientific and technical staff working to characterize and understand the variation in plant traits as part of the Center for Bioenergy Innovation (cbi.ornl.gov) and partially supported by a DRD project entitled “Bringing the Digital Underground to the Advanced Plant Phenotyping Laboratory.”

 

Key Research Areas:

The selected candidate will be based in the Plant Systems Biology group, which is undertaking broad, integrated approaches to enhance fundamental understanding of genetic controls of plant productivity, biomass quality and sustainability, and carbon sequestration using bioenergy-relevant plants. The specific project will be part of the Center of Bioenergy Innovation that focuses on designing biological feedstocks for mass production of sustainable aviation fuel. The PIs are John Lagergren and Larry York, bringing expertise in shoot and root phenomics, computer vision, and artificial intelligence. The project will focus on research and development of advanced machine learning and deep learning algorithms to analyze large quantities of multimodal images and data arising from the Advanced Plant Phenotyping Laboratory (www.ornl.gov/appl), which will be upgraded to image roots during the project.

 

ORNL has world-class computational facilities to utilize in this project, from daily access to an NVIDIA DGX A100 system for prototyping neural networks, to the latest NVIDIA H100 architectures for limited projects, and the potential to scale algorithms (e.g., foundation models) on the fastest high-performance computing (HPC) system for open science in the world, Frontier. The candidate will join a team of computer vision, machine learning, and data science experts to accomplish project goals. The candidate will learn state-of-the-art deep learning methods and the associated biological domain knowledge to ensure the accuracy and validity of model results. Importantly, this team has a track record of real-world impact with tools in use around the world and significant contributions to biological knowledge through use of computing and phenomics.

 

Major duties/responsibilities include:

• Lead research and development of deep learning models for segmentation, classification, object detection, etc., applied to multimodal time series image data
• Contribute to the development of multimodal foundation models for plant science using data from APPL and additional internal and external data sources
• Collaborate with internal and external researchers on a variety of AI/ML-related biological research and projects, e.g., contributing to the next generation RhizoVision Explorer (www.rhizovision.com), understanding plant function through utilizing the latent space and direct correlations to genetic information, etc.
• Present research progress in project meetings and national/international conferences
• Publish scientific results in peer-reviewed journals in a timely manner
• Maintain detailed and accurate records
• Acquire and analyze data in keeping with project scheduling
• Ensure compliance with environmental, safety, health and quality program requirements
• Maintain strong dedication to the implementation and perpetuation of values and