Post-Doctoral Associate - Pickering Lab

Post‑Doctoral Associate - Pickering Lab

Department: CAES-Crop & Soil Sciences

Agentic AI is rapidly changing nearly every domain, from academia to industry. Agriculture is no different. This 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 Post‑Doctoral 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.

• A working agentic modeling stack demonstrated on at least one ''end‑to‑end'' crop use case (e.g., data > genomics predictions + AI‑CGM > intervention suggestions with uncertainty).
• Publications in top venues (ML for science, computational biology, agronomy/crop modeling) and public releases of code/benchmarks.
• Clear pathways to stakeholder deployment (breeders, agronomists, extension, or industry R&D).

Mathematical + computational depth, especially one or more of:

• Dynamical systems, scientific computing, numerical methods, optimization
• Probabilistic modeling / Bayesian methods / uncertainty quantification
• Representation learning for sequences/graphs; geometric deep learning

Proficiency (or strong interest) in any of:

• Genomics, quantitative genetics, genomic prediction, GWAS, multi‑omics integration
• Crop growth modeling, ecophysiology, spatiotemporal modeling, remote sensing + agronomy
• Agentic AI / tool‑using LLM systems / workflow orchestration for science

Candidates should have strength in several of the following:

• Machine learning / deep learning; LLMs, GNNs, sequence models; hybrid modeling
• Linear algebra, optimization, probabilistic modeling, experimental design, active learning
• Scientific programming in Python (other languages a bonus); building maintainable, open‑source codebases and reproducible pipelines (containers, workflows, benchmarking)
• Ability to collaborate across disciplines and communicate clearly with both technical and domain audiences

Physical Demands: Lifting 25 lbs, prolonged sitting at office desk.

Is this a Position of Trust?: Yes

Does this position have operation, access, or control of financial resources?: No

Does this position require a P-Card?: No

Is driving a requirement of this position?: No

Does this position have direct interaction or care of children under the age of 18 or direct patient care?: No

Does this position have Security Access (e.g., public safety, IT security, personnel records, patient records, or access to chemicals and medications): Yes

Offers of employment are contingent upon completion of a background investigation…