Post-Doctoral Associate - Pickering Lab

Post-Doctoral Associate - Pickering Lab Below you will find the details for the position including any supplementary documentation and questions you should review before applying for the opening. To apply for the position, please click the Apply for this Job link/button. If you would like to bookmark this position for later review, click on the Bookmark link. If you would like to print a copy of this position for your records, click on the Print Preview link. Posting Details Position Details Posting Number: G/R32483P Working Title: Post-Doctoral Associate - Pickering Lab Department: CAES-Crop & Soil Sciences About the University of Georgia: Chartered by the state of Georgia in 1785, the University of Georgia is the birthplace of public higher education in America and is the state’s flagship university. UGA is ranked among the top 20 public universities in U.S. News & World Report. The main campus is in Athens, with extended campuses in Atlanta, Griffin, Gwinnett, and Tifton. UGA employs approximately 3,100 faculty and more than 7,700 full-time staff. The University’s enrollment exceeds 41,000 students including over 31,000 undergraduates and over 10,000 graduate and professional students. Academic programs reside in 19 schools and colleges, including the School of Medicine. College/Unit/Department Website: (provided in posting) Employment Type: Employee Additional Schedule Information: Monday through Friday 8AM-5PM. Occasional travel for conferences. Advertised Salary: Commensurate with Experience Anticipated Start Date: 06/01/2026 Posting Date: 03/19/2026 Closing Date: Open Until Filled Location of Vacancy: Athens Area EEO Statement The University of Georgia is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to age, color, disability, genetic information, national origin, race, religion, sex, or veteran status or other protected status. Persons needing accommodations or assistance with the accessibility of materials related to this search are encouraged to contact Central HR (View email address on click.appcast.io). USG Core Values The University System of Georgia is comprised of 25 institutions of higher education and learning, and the USG Statement of Core Values includes Integrity, Excellence, Accountability, and Respect. More details on the USG Core Values and Code of Conduct are available in USG Board Policy 08.02.18.01.02. Duties/Responsibilities Develop Agentic AI systems for agricultural design and prediction 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. Create benchmarks, datasets, and evaluation protocols Reproducible benchmarks across crops, environments, and tasks; rigorous ablations; robustness + generalization testing. Career development & scholarly dissemination Papers, talks, open-source releases, mentoring students, and participating in interdisciplinary collaborations. Position Details Position Information Classification Title: Post-Doctoral Associate AD, FLSA: Exempt Job Code: FTE 1.00 Minimum Qualifications Agentic AI is rapidly changing nearly every domain, from academia to industry. This postdoctoral opportunity will research and build agentic scientific AI systems that can design, predict, and optimize agricultural outcomes—across crops, environments, and management regimes. We seek 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 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. Potential Focus Areas 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. What Success Looks Like (12–24 months) A working agentic modeling stack demonstrated on at least one end-to-end crop use case (data → genomics predictions + AI-CGM → intervention suggestions with uncertainty). Publications in top venues and public releases of code/benchmarks. Clear pathways to stakeholder deployment (breeders, agronomists, extension, or industry R&D). Qualifications Relevant/Preferred Education, Experience, Licensure : in-position mathematical and 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 Knowledge, Skills, Abilities and/or Competencies 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; building maintainable, open-source code bases and reproducible pipelines Ability to collaborate across disciplines and communicate with 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 having a P-Card an essential function of this position? 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? Yes Background Investigation: Offers of employment are contingent upon completion of a background investigation including a criminal background check, confirmation of credentials and employment history, and, if applicable, a satisfactory credit check. A pre-employment drug test may be required for high-risk roles. Please visit the UGA Background Check website. Contact Information Recruitment Contact Name: Ethan Pickering Email: View email address on click.appcast.io Phone: Applicant Documents Required Documents : Resume/CV, Sample Publications Optional Documents : Cover Letter, List of References with Contact Information, Other Documents #1 #J-18808-Ljbffr University of Georgia