Post-Doctoral Associate - Pickering Lab

Posting Information

University of Georgia

Posting Number: G/R32483P

Working Title: Post-Doctoral Associate - Pickering Lab

Department: CAES-Crop & Soil Sciences

Employment Type: Employee

Schedule: Monday through Friday 8AM-5PM. Occasional travel for conferences.

Salary: Commensurate with Experience

Anticipated

Start Date:

06/01/2026

Posting Date: 03/19/2026

Open Until Filled: Yes

Location of Vacancy: Athens Area

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Agentic AI is rapidly changing nearly every domain, from academia to industry. Agriculture is no different. These postdoctoral opportunity will look to research and build agentic scientific AI systems that can design, predict, and optimize agricultural outcomes—across crops, environments, and management regimes. We are seeking a Postdoctoral Associate to develop the next generation of Agentic AI for Agricultural Design and Prediction, spanning:

• Genomics agents that assemble AI-native genomic prediction and selection models (e.g., DNA foundation-models, GNN/sequence architectures for breeding decisions, pangenomic models).
• Crop Growth Model agents that create AI-native crop growth models—including Bio-Informed Neural Networks (BINNs) and hybrid dynamical systems that fuse mechanistic constraints with large-scale data.
• Scientific agent workflows that can ingest literature + datasets, propose modeling choices, run experiments, quantify uncertainty, and iteratively improve models with human-in-the-loop evaluation.

This role sits at the intersection of applied mathematics, machine learning, genomics, crop science, and dynamical systems, and will be carried out in a highly interdisciplinary team environment.

Percentage Of Time: 70%

• Architect agent workflows (data/literature ingestion → modeling → evaluation → iteration) for genomics and crop modeling.
• Foundation-model or representation-learning approaches for genotype/sequence/omics; uncertainty-aware prediction; decision support for selection.
• Hybrid mechanistic + learned models; neural ODEs / constrained learning; spatiotemporal modeling across G×E×M.

Percentage Of Time: 15%

• Reproducible benchmarks across crops, environments, and tasks; rigorous ablations; robustness + generalization testing.

Percentage Of Time: 15%

• Papers, talks, open-source releases, mentoring students, and participating in interdisciplinary collaborations.

• Agentic Genomics for Prediction & Selection:
  Build agents that can automatically construct, evaluate, and adapt genomic/pangenomic/editing prediction pipelines (from raw genotypes/omics to breeding-value predictions), including modern representation learning and uncertainty-aware decision support.
• Agentic AI Crop Growth Models (AI-CGMs):
  Develop hybrid modeling agents that learn AI-native CGMs (e.g., BINNs; constrained neural ODEs; spatiotemporal models) integrating genomics, phenomics, physiology, weather, soils, remote sensing, and management data.