Full-Time

Research Specialist 3

Updated on 5/8/2026

Deadline 5/20/26
SDSU Research Foundation

SDSU Research Foundation

Compensation Overview

$5.9k - $6.3k/mo

Imperial, CA, USA

In Person

Must reside in California and be within commutable distance to SDSU at time of hire.

Category
Lab & Research (2)
,
Required Skills
Python
Machine Learning
MATLAB
Requirements
  • Equivalent to graduation from a four year university.
  • Three years of experience in technical research or statistical work experience.
  • Experience with indoor air quality monitoring, using research (e.g., SidePak, OPS) and low-cost air sensors (e.g., PurpleAir, Atmotube).
  • Familiar with Python and MATLAB programming languages and machine learning model training and inference procedures (e.g., logistic regression, artificial neural network).
Responsibilities
  • Take the lead on laboratory experiments 40%: Purchase requisition for commercial vape detectors and consumer air monitors; Purchase requisition for vaping, smoking, and other indoor source materials; Prepare and organize instruments in the experimental room; Perform indoor emission tests for different indoor sources (e.g., vaping, smoking, cooking, dusting); Evaluate performance of commercial vape detectors (sensitivity and specificity); Assess potential of consumer air monitors in identifying vaping (sensitivity and specificity); Instruct two graduate students for indoor emission testing; Ensure personnel safety during emission testing (e.g., N95 mask, exposure alert system); Ensure a rigorous scientific protocol for experimental procedures; Discuss and update experimental results with PI.
  • Conduct machine learning computer modeling 40%: Compile and synchronize real-time measurements of air monitors in each experiment; Label air monitoring data based on emission sources (e.g., vaping, smoking); Aggregate data across different emission experiments based on source types; Transform measurement profiles into normalized features for machine learning; Construct training, cross-validation, and test data sets for each air monitor; Perform hyperparameter tuning for neural network modeling (e.g., systematically testing different numbers of hidden layer/units, activation functions, regularization parameters, learning rates, batch sizes, numbers of iterations); Train optimal neural network source identification models for each air monitor; Test model classification performance (accuracy, precision, recall, F-score); Compare optimal neural net models with other machine learning models; Upload developed ML algorithms to mini PC (e.g., raspberry pi) for real-time source alerts; Assist PI with journal publications on performance of commercial vape detectors and the newly developed low-cost vape alert system; Assist PI with annual reports for the two-year project; Co-instruct two graduate students for experimental work; Co-instruct two graduate students for labeling the source-specific data.
  • Supervise graduate students 10%: Co-instruct two graduate students for experimental work; Co-instruct two graduate students for labeling the source-specific data.
Desired Qualifications
  • Experienced with field measurements of secondhand smoke and vape
  • Familiar with Logistic and Artificial Neural Network modeling
  • Published scientific journal(s) to support the qualifications above
  • Professional Engineer license in Environmental Engineering
SDSU Research Foundation

SDSU Research Foundation

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