Summer Researcher

Essential Functions

• Design, fabricate, and characterize electrochemical microfluidic gas sensing cells incorporating ionic liquid electrolytes.
• Develop and optimize electrode materials, including indium tin oxide (ITO) and carbon-based electrodes, to enhance sensor sensitivity and selectivity.
• Integrate porous membrane transducer architectures into portable, field-deployable sensing modules.
• Conduct impedance spectroscopy and amperometric measurements for volatile organic compound (VOC) detection and quantification.
• Implement and refine machine learning models, including neural networks and random forests, for multi-analyte classification and concentration prediction.
• Develop signal drift correction protocols to improve long-term sensor stability under real-world operating conditions.
• Integrate sensor data acquisition with embedded computing platforms to support autonomous, portable operation.
• Design and execute experiments to evaluate sensor performance against established reference methods.
• Document experimental procedures and results, prepare technical reports, and contribute to presentations.
• Provide technical data and analytical results to support ongoing development of the ESSENCE-Air platform.
• Collaborate with graduate and undergraduate students within the PRANA Lab.

Prerequisite Qualifications

• Ph.D. in Chemical Engineering, Materials Science, Electrical Engineering, Chemistry, or a related field.
• Demonstrated hands-on experience with electrochemical sensing techniques, including impedance spectroscopy, cyclic voltammetry, and amperometry.
• Experience designing and fabricating microfluidic devices.
• Ability to work independently, troubleshoot technical challenges, and deliver results within established project timelines.
• Excellent written and verbal communication skills in English.

Preferred Qualifications

• Experience with ionic liquid-based electrolyte systems for gas sensing applications.
• Familiarity with AI/ML frameworks such as Python, scikit-learn, TensorFlow, or equivalent tools applied to chemical sensor data.
• Experience developing portable or field-deployable analytical instrumentation.
• Experience with impedance data analysis and equivalent circuit modeling.
• Background in point-of-care diagnostics or environmental sensing applications.