Postdoctoral Research Scholar

Position Information

PG191786PD

Post Doc

Exempt

118101 - Biological And Agricultural Engineering

118101 - Biological And Agricultural Engineering

Raleigh, NC

The Department of Biological and Agricultural Engineering at North Carolina State University invites applications for a full-time Postdoctoral Research Associate to work with Dr. Daniela Jones, Associate Professor, in machine learning, AI surrogate modeling, and scientific model validation for large-scale agricultural and bioeconomy systems. The position supports REFRAME (Resource Engineering Framework for Responsible, Augmented Modeling and Engagement), a multi-year initiative funded by Schmidt Sciences through the Virtual Institute for Feed stocks of the Future ( VIFF ).

REFRAME is developing a modular, AI-enabled, open-source platform that integrates legacy scientific models, artificial intelligence, optimization, and shared data infrastructure to evaluate future biomass systems across availability, accessibility, characterization, conversion, and impacts. The successful candidate will serve as REFRAME’s scientific lead for AI surrogate methodology, providing project-wide leadership in the development, statistical validation, benchmarking, deployment, and long-term evolution of scalable AI surrogate models.

Housed within REFRAME’s Modeling Pillar, the researcher will work at the intersection of AI and agricultural domain science and collaborate with an interdisciplinary team of graduate students, postdoctoral researchers, faculty, software developers, the Platform Architect, and researchers within the Digital Backbone and Stakeholder Engagement Pillars. The position will also contribute to AI agents for model orchestration, scientific workflow automation, and stakeholder-facing decision support.

The position is expected to begin in Spring 2027, with an initial one-year appointment and the potential for extension based on performance and funding availability.

• Lead the development and implementation of project-wide methodologies for the development, evaluation, statistical validation, benchmarking, and deployment of scalable and scientifically rigorous AI surrogate models across REFRAME’s modeling domains.
• Develop generalized surrogate modeling and statistical evaluation frameworks for computationally intensive scientific models, including approaches for calibration, uncertainty quantification, generalization and robustness assessment, and explainable AI.
• Apply knowledge of agricultural production systems, agronomic practices, crop and soil processes, and biomass systems to evaluate the scientific and practical plausibility of surrogate model behavior and outputs.
• Collaborate with and mentor interdisciplinary researchers developing AI surrogates while establishing standardized, reproducible scientific machine learning workflows across REFRAME .
• Collaborate with the Digital Backbone team to integrate validated surrogates into scalable, open-source infrastructure and contribute to AI agents for model orchestration, workflow automation, and scientific decision support.
• Lead and contribute to peer-reviewed publications and conference presentations, and contribute to research proposal development and the pursuit of external funding opportunities that advance REFRAME and related research

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