Research Specialist III

Under direction, the incumbent works independently in the preparation of detailed plans for the research study, the compilation and interpretation of data, and the preparation of detailed reports on phases of major studies/research studies. Responsible for supervising and directing a small work group consisting of students, Grad Students, Research Assistants and lower level Research Specialists engaged in the compilation and analysis of data.This advanced-level position for incumbents who can provide program leadership with a higher level of scope and responsibilities which requires a specialized knowledge, skill and experience relevant to the program/project.

The Research Specialist III is responsible for but not limited to:

The position will take the lead on laboratory experiments testing air sensor performances (sensitivity and specificity) in identifying vaping, smoking and other indoor emissions. The position will also perform machine learning modeling in this project, especially for the intensive hyperparameter tuning procedure for neural network models. It will involve co-instructing two graduate students for emission testing and assisting PI with the preparations of journal publications and annual reports for the two-year project.

The duties of this position will vary in intensity depending on need, but will primarily be divided as follows:

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 for the project _10_%

• →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

Supervise graduate students _10_%

• →Co-instruct two graduate students for experimental work
• →Co-instruct two graduate students for labeling the source-specific data

Equivalent to graduation from a four year university.

Three years of experience in technical research or statistical work experience.

The candidate should be experienced with indoor air quality monitoring, using research (e.g., SidePak, OPS) and low-cost air sensors (e.g., PurpleAir, Atmotube). He/she should be familiar with Python and MATLAB programming languages and machine learning model training and inference procedures (e.g., logistic regression, artificial neural network).

-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 (PE) license in Environmental Engineering

• Candidate must reside in California and live within a commutable distance from SDSU at time of hire.
• Job offer is contingent upon satisfactory clearance based on background check results (including a criminal record check).
• San Diego State University Research Foundation is an equal opportunity employer. Consistent with California law and federal civil rights laws, SDSU Research Foundation provides equal opportunity in employment without unlawful discrimination or preferential treatment based on race, sex, color, ethnicity, or national origin or any other categories protected by federal or state law.  
• Employment decisions are based on an individual’s qualifications as they relate to the job under consideration. Our commitment to equal opportunity means ensuring that every employee has equal access to resources and support. 
• SDSU Research Foundation complies with Titles VI and VII of the Civil Rights Act of 1964, Title IX of the Education Amendments of 1972, the Americans with Disabilities Act (ADA), Section 504 of the Rehabilitation Act, the California Equity in Higher Education Act, California’s Proposition 209 (Art. I, Section 31 of the California Constitution), and other applicable state and federal anti-discrimination laws including grant or contract terms and conditions related to funded program activities. Further the SDSU Research Foundation maintains a Nondiscrimination Policy that prohibits discriminatory preferential treatment, segregation based on race or any other protected status, and all forms of unlawful discrimination, harassment, and retaliation in all programs, policies, and practices.  
• SDSU Research Foundation makes all employment decisions including, but not limited to, applicant screening, hiring, promotion, demotion, compensation, benefits, disciplinary actions, and terminations on the basis of merit.