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University of Minnesota

Post-Doctoral Associate (# 376248)

Closes:

Salary: $62,232 – $70,000 per year

59
Job ID: 376248

About the Job

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

Please note, this position is not eligible for visa sponsorship including 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-making for agricultural, food, biological, energy, environmental, and materials systems. The lab's core 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. The successful candidate will primarily advance data analysis, algorithm development, modeling, and translation of complex measurements into practical decision-support, process-optimization, and adaptive-control tools, while working closely with experimental and domain collaborators as needed. Candidates with strong computational backgrounds who are interested in learning new sensing or application domains are encouraged to apply.

Duties/Responsibilities

    65% Research and technical development. The primary focus of this postdoctoral associate will be the development and application of data-science, statistical, machine-learning, deep-learning, and decision-oriented methods for complex scientific and engineering datasets. Major tasks may include developing models for classification, regression, anomaly detection, forecasting, process monitoring, and control; integrating multimodal data from imaging, spectroscopy, IoT sensors, remote sensing,…

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