Postdoctoral Research Associate: AI, Data Science, and Intelligent Sensing for Agricultural, Food, and Sustainable Systems (SAGE Lab)

About the role

The SAGE Lab (Sensing, AI & Green Systems Engineering) in the Department of Bioproducts and Biosystems Engineering at the University of Minnesota Twin Cities is seeking a full-time Postdoctoral Research Associate. The lab develops trustworthy, data-driven methods that connect sensing, artificial intelligence, and decision-making for agricultural, food, biological, energy, environmental, and materials systems. Methodological focus includes statistical and machine learning, deep learning, chemometrics, multimodal data fusion, computer vision, uncertainty-aware modeling, stochastic control, optimization, and deployable edge-to-cloud decision systems. Spectroscopy, hyperspectral imaging, remote sensing, IoT, and process measurements are important data sources within this broader framework rather than requirements defining a single experimental profile. Candidates with strong computational backgrounds who are interested in learning new sensing technologies or application domains are encouraged to apply.

Responsibilities

• Research and Technical Development (65%): Develop and apply statistical, machine-learning, deep-learning, and decision-oriented methods for complex scientific and engineering datasets; develop models for classification, regression, anomaly detection, forecasting, process monitoring, and control; integrate multimodal data from imaging, spectroscopy, IoT sensors, remote sensing, environmental measurements, and process systems; develop robust methods for small, noisy, incomplete, heterogeneous, or high-dimensional datasets; evaluate uncertainty, calibration, generalization, transfer learning, domain adaptation, and sensor fusion; develop stochastic-control, optimization, and decision-making approaches for systems operating under uncertainty; build reproducible, deployment-oriented workflows for real-time, automated, edge, and cloud-based applications; contribute to experimental design, data acquisition, sensor integration, or laboratory/field validation depending on project needs and background; lead and co-author peer-reviewed scientific manuscripts.
• Proposal Development, Scientific Writing, and Publication (25%): Work closely with the PI to strengthen the SAGE Lab's externally funded research portfolio; identify and evaluate federal, state, foundation, and industry funding opportunities; develop research concepts, hypotheses, objectives, and technical approaches; contribute to computational and experimental plans, milestones, timelines, and deliverables; generate and analyze preliminary data; prepare figures, schematics, tables, and technical materials; draft and revise technical sections of competitive grant and contract proposals; lead and co-author peer-reviewed publications; strong scientific writing expected; prior grant-writing experience highly desirable but not required.
• Collaboration, Mentoring, and Research Dissemination (10%): Participate actively in SAGE Lab and departmental activities; collaborate with faculty, interdisciplinary research teams, and external partners; mentor undergraduate and graduate researchers; contribute to technical reports, invention disclosures, conference abstracts, presentations, seminars, and professional meetings.

Required Qualifications

• Ph.D. in engineering, computer science, or a closely related discipline.
• Research experience developing and applying statistical, machine-learning, deep-learning, chemometric, or related computational methods to imaging, spectral, sensor, process, environmental, or other complex scientific data.
• Strong quantitative data-analysis and scientific-programming skills using Python, MATLAB, R, or an equivalent platform.
• Experience developing reproducible computational workflows.
• Demonstrated ability to conduct independent research, formulate data-driven research questions, rigorously evaluate models, and communicate results through scientific writing and presentations.
• Ability to connect modeling assumptions and outputs to the physical, biological, or process systems generating the data.
• Excellent oral and written scientific communication skills.
• Ability to work effectively in an interdisciplinary and collaborative research environment.
• Willingness to learn new computational, sensing, experimental, and proposal-development methods.
• Direct hands-on experimental experience is welcome but is not required for all applicants.

Preferred Qualifications

• Experience in one or more of the following areas: multimodal sensing and sensor fusion; computer vision; time-series analysis; uncertainty quantification and model calibration; transfer learning and domain adaptation; stochastic control and optimization; decision-making under uncertainty; machine learning for small, noisy, incomplete, or high-dimensional datasets; real-time, automated, edge, or deployment-oriented analytics and decision systems; spectroscopy and hyperspectral/multispectral imaging; RGB/thermal imaging; IoT sensors and remote sensing; agricultural, food, biological, environmental, biomass/bioenergy, circular-economy, materials, or related sustainable systems; competitive research proposal and grant development; peer-reviewed scientific publishing and student mentoring.

Pay

Salary: $62,232–$70,000 annually, depending on education, qualifications, and experience. Visa sponsorship is not available for this position (including H-1B or Green Card sponsorship).

Schedule

This is a 100% full-time position with a one-year initial appointment, with the possibility of extension based on satisfactory performance and continued funding.