Postdoctoral Research Associate: AI, Data Science, and Sensing Agricultural, Food

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

Please note, this position is not eligible for visa sponsorshipincluding H-1B or Green Card sponsorship.

This position will provide scientific and technical support for Dr.Sambandh Dhal’s SAGE Lab(Sensing, AI & Green Systems Engineering) in the Department of Bioproducts and Biosystems Engineering at the University of Minnesota.

The SAGE Lab develops trustworthy, data-driven methods that connect sensing, artificial intelligence, and decision-makingfor agricultural, food, biological, energy, environmental, and materials systems. The lab's core methodological focus includesstatistical and machine learning, deep learning, chemometrics,multimodal data fusion, computer vision, uncertainty-awaremodeling, stochastic control, optimization, and deployableedge-to-cloud decision systems. Spectroscopy, hyperspectralimaging, remote sensing, IoT, and process measurements are important data sources within this broader framework rather thanrequirements defining a single experimental profile.

The successful candidate will primarily advance data analysis, algorithmdevelopment, modeling, and translation of complex measurements into practical decision-support, process-optimization, and adaptive-control tools, while working closely with experimental anddomain collaborators as needed. Candidates with strongcomputational backgrounds who are interested in learning newsensing or application domains are encouraged to apply.

• 65% Research and technical development. The primary focus ofthis postdoctoral associate will be the development and application of data-science, statistical, machine-learning, deep-learning, and decision-oriented methods for complex scientific and engineeringdatasets. Major tasks may include developing models forclassification, regression, anomaly detection, forecasting, process monitoring, and control; integrating multimodal data from imaging,spectroscopy, IoT sensors, remote sensing, environmentalmeasurements, and process systems; developing robust methods forsmall, noisy, incomplete, heterogeneous, or high-dimensionaldatasets;
  
  evaluating uncertainty, calibration, generalization,transfer learning, domain adaptation, and sensor fusion; developingstochastic-control, optimization, or decision-making approaches for systems operating under uncertainty; and building reproducible,deployment-oriented workflows for real-time, automated, edge, orcloud-based applications. Depending on project needs and the candidate's background, the postdoctoral associate may also contribute to experimental design, data acquisition, sensor integration, or laboratory/field validation, either directly or inclose collaboration with experimental researchers. The candidate will be expected to ensure that modeling assumptions and outputsremain grounded in the constraints and behavior of the systems being studied and will lead and co-author peer-reviewed manuscriptsarising from these research activities.
• 25% Proposal development, scientific writing, and publication.
  
  The postdoctoral associate will work closely with the PI to strengthen the SAGE Lab's externally funded research portfolio and publication record. Responsibilities may include identifying and evaluating federal, state, foundation, and industry funding opportunities; conducting targeted literature and programmaticreviews; developing research concepts, hypotheses, specific aims,objectives, and technical approaches; contributing to experimental and computational plans, work packages, milestones, timelines, and deliverables; generating and analyzing preliminary data; preparingfigures, schematics,…