Postdoctoral Associate – Instrumentation and Sensing/Bioelectronics Workflow Validation

Virginia Tech • Blacksburg, VA

1d • $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 in the Programmable Cloud Laboratory (PCL) Node. The research will focus on developing and experimentally validating AI-guided design-build-test-learn (DBTL) workflows for plasmonic sensing and flexible bioelectronics, based on project priorities and the candidate’s expertise.

The Postdoctoral Associate will investigate experimental methods connecting materials and device fabrication with calibrated optical, electrical, electrochemical, or multimodal measurements. Work includes synchronized data acquisition, reliability assessment, uncertainty-aware model validation, and reproducible experiment records. The position contributes to validated materials-, geometry-, interface-, and process-to-sensing relationships; digital-twin validation; and machine-readable provenance linking samples, fabrication, measurements, models, and uncertainty estimates.

The Postdoctoral Associate will report to the PCL Node’s Deputy Director and conduct research under designated faculty mentors. The role involves collaboration with faculty, researchers, and technical personnel while developing increasingly independent research expertise. Opportunities include peer-reviewed publications, conference presentations, student mentoring under faculty supervision, professional-development planning, and participation in proposals or fellowships 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 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.
• Ability to develop, validate, and document reproducible experimental methods, including calibration, repeatability, quality control, reliability, or uncertainty assessment.
• 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 across engineering, biomedical sciences, materials science, chemistry, or related disciplines.

Responsibilities

• Conduct mentored research to develop and experimentally validate AI-guided design-build-test-learn workflows for plasmonic sensing and flexible bioelectronics.
• Investigate experimental methods linking materials and device fabrication with calibrated optical, electrical, electrochemical, or multimodal measurements.
• Work on synchronized data acquisition, reliability assessment, uncertainty-aware model validation, and reproducible experiment records.
• 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.

What This Job Offers

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