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Cal Poly Pomona Foundation

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Undergrad Research Tech - Machine Learning #755401345

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Summary

Undergrad CS student research role at Cal Poly Pomona (Oct 15–Dec 15, 2026, $20/hr, up to 20 hrs/week) building the ML pipeline for an indoor-crop lab: extracting Raspberry Pi sensor time series, forecasting growing conditions, detecting sensor faults, and classifying crop photos in Python.

SUMMARY: Cal Poly Pomona Enterprises is hiring one undergraduate Computer Science student for a fall research position applying machine learning to indoor crop production data under the mentorship of Dr. Eshwar Ravishankar, Assistant Professor in Plant Science. The lab runs mushroom, microgreen, and lettuce growing systems controlled by Raspberry Pi computers that continuously log temperature, humidity, carbon dioxide, light, and water quality. The technician will turn these records, together with harvest data and crop photographs, into models that forecast growing conditions, flag failing sensors, and predict crop outcomes. The position pays $20 per hour and runs from October 15 to December 15, 2026.

SOME OF THE THINGS THAT YOU WILL GET TO DO (this is just a small sampling of work variety):

The research technician will build the data and modeling pipeline for the lab's growing systems, working alongside plant science students who run the crops. Specific responsibilities include:

  • Extract sensor time series from the lab's Raspberry Pi controllers and their time series database, and build clean, analysis-ready datasets in Python.
  • Align sensor records with cultivation events and harvest data, such as planting, inoculation, and harvest dates and yield weights, recorded by the plant science technicians.
  • Train and evaluate forecasting models that predict temperature, humidity, and carbon dioxide several hours ahead, to support earlier control decisions.
  • Develop anomaly detection methods that flag sensor faults, equipment failures, and conditions drifting outside target ranges.
  • Train image classification models on crop photographs to identify growth stage and early signs of contamination.
  • Explore models that predict yield and harvest timing from environmental history.
  • Evaluate every model on held-out data, compare it against simple baseline methods, and explain the results to the plant science team.
  • Write documented, reproducible Python code in a shared Git repository, and build simple dashboards or reports that summarize model output.
  • Contribute to an end-of-semester research report and present results at an end-of-semester lab review.

The position offers up to 20 hours per week, with technicians working closely with supervisors and reporting any issues promptly. This is a temporary position from October 15 to December 15, 2026. Compensation is $20 per hour.

MINIMUM EDUCATION AND EXPERIENCE:

  • Must be a current undergraduate student at Cal Poly Pomona in Computer Science or a related field.
  • Proficiency in Python and coursework or project experience in machine learning, using libraries such as scikit-learn, PyTorch, or TensorFlow.
  • Experience cleaning and analyzing data with pandas and NumPy.
  • Familiarity with version control using Git.
  • Experience with time series forecasting, computer vision, or the Linux command line is preferred but not required.
  • Interest in agriculture, environmental sensing, or applied research. No plant science background is required.
  • Ability to explain technical results clearly to collaborators from other fields.
  • Strong attention to detail, motivated, and shows initiative.

CSU along with Cal Poly Pomona Enterprises strongly recommend faculty, staff, and students who are accessing campus facilities to be immunized against COVID-19 or declare an exemption from doing so. The systemwide policy can be found at COVID-19 Vaccination Interim Policy and any questions may be sent to fdnhr@cpp.edu.

Cal Poly Pomona Enterprises is an Equal Opportunity Employer. In compliance with the Americans with Disabilities Act, Cal Poly Pomona Enterprises may provide reasonable accommodations to qualified individuals with disabilities and encourages both prospective and current employees to discuss potential accommodation with the employer.

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