Postdoctoral Associate – Instrumentation and Sensing/Bioelectronics Workflow Validation

Virginia Tech • Blacksburg, VA

3d • $65,000 - $75,000

About The Position

The Bradley Department of Electrical and Computer Engineering at Virginia Tech seeks a Postdoctoral Associate to conduct mentored research developing and experimentally validating AI-guided design-build-test-learn (DBTL) workflows for plasmonic sensing and flexible bioelectronics, based on project priorities and candidate expertise, within the Programmable Cloud Laboratory (PCL) Node.

Reporting to the PCL Node's Deputy Director and working under designated faculty mentors, the associate will investigate experimental methods linking materials and device fabrication to calibrated optical, electrical, electrochemical, or multimodal measurements. Research areas include synchronized data acquisition, reliability assessment, uncertainty-aware model validation, reproducible experiment records, validated materials-, geometry-, interface-, and process-to-sensing relationships, digital-twin validation, and machine-readable provenance connecting samples, fabrication, measurements, models, and uncertainty estimates.

The successful candidate will collaborate with faculty, researchers, and technical personnel while developing increasingly independent research expertise. The appointment offers opportunities for peer-reviewed publications, conference presentations, student mentoring under faculty supervision, professional-development planning, and proposal or fellowship participation as appropriate. Routine preventive maintenance, service-center administration, instrument scheduling, recurring general troubleshooting, and independent authorization of facility access are not primary responsibilities; facility and instrument activities will be coordinated with authorized personnel.

Requirements

• PhD in electrical engineering, biomedical engineering, chemical engineering, mechanical engineering, applied physics, materials science, chemistry, physics, or a closely related field.
• PhD must have been awarded no more than four years before the effective appointment date, with at least one year of eligibility remaining.
• Demonstrated research experience in nanofabrication, sensing technologies, flexible electronics, bioelectronics, or a closely related area, documented through doctoral research, publications, or research projects.
• Experience conducting experimental research involving device fabrication, sensing measurements, materials characterization, or electronic device testing.
• Experience using laboratory instrumentation, calibration methods, metrology tools, or device-characterization techniques in experimental research.
• Experience collecting, analyzing, and interpreting experimental data using scientific computing or data-analysis methods.
• Demonstrated ability to develop, validate, and document reproducible experimental methods, including calibration, repeatability, quality control, reliability, or uncertainty assessment.
• Demonstrated ability to communicate research methods and findings through technical reports, scholarly publications, or research presentations.
• Ability to work collaboratively and safely in shared research facilities and coordinate with faculty mentors, researchers, and facility-authorized personnel.

Nice To Haves

• Research experience in plasmonic sensing, surface-enhanced Raman spectroscopy (SERS), optical sensing, bioelectronics, or related hybrid optical-electrical biosensing technologies.
• Experience with atomic layer deposition (ALD), thin-film processing, polymer materials, flexible electronic devices, or neural interfaces.
• Experience with optical, electrical, electrochemical, impedance, or multimodal device measurements.
• Experience with automated experimentation, instrument control, synchronized data acquisition, or process monitoring.
• Familiarity with digital twins, AI-guided experimentation, optimization methods, or uncertainty quantification.
• Experience using Python, MATLAB, or comparable programming tools for scientific computing, instrument automation, or experimental data analysis.
• Experience evaluating device reliability, measurement reproducibility, or experimental performance.
• Experience with structured metadata, experiment manifests, research-data provenance, FAIR or AI-ready datasets, student mentoring, SOP development, or research-user support.
• Demonstrated experience collaborating with researchers across engineering, biomedical sciences, materials science, chemistry, or related disciplines.

Responsibilities

• Conduct mentored research developing and experimentally validating AI-guided DBTL workflows for plasmonic sensing and flexible bioelectronics.
• Investigate experimental methods connecting materials and device fabrication with calibrated optical, electrical, electrochemical, or multimodal measurements.
• Develop and apply synchronized acquisition, reliability assessment, uncertainty-aware model validation, and reproducible experiment-record methods.
• Contribute to validated materials-, geometry-, interface-, and process-to-sensing relationships and digital-twin validation.
• Develop machine-readable provenance connecting samples, fabrication, measurements, models, and uncertainty estimates.
• Collaborate with faculty, researchers, technical personnel, and facility-authorized personnel.
• Communicate research through peer-reviewed publications and conference presentations.
• Mentor students under faculty supervision and participate in professional-development planning and proposal or fellowship activities as appropriate.
• Coordinate facility and instrument activities with authorized personnel.

Benefits

• Opportunities for peer-reviewed publications and conference presentations.
• Opportunities for student mentoring under faculty supervision.
• Professional-development planning and opportunities to participate in proposals or fellowships as appropriate.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree