Job Description

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

Reporting to the PCL Node's Deputy Director and conducting research under the guidance of designated faculty mentors, the Postdoctoral Associate will investigate experimental methods connecting materials and device fabrication with calibrated optical, electrical, electrochemical, or multimodal measurements; synchronized acquisition; reliability assessment; uncertainty-aware model validation; and reproducible experiment records. The position will contribute to 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 will provide 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.

Required Qualifications

• PhD in electrical engineering, biomedical engineering, chemical engineering, mechanical engineering, applied physics, materials science, chemistry, physics, or a closely related field.
• PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining.
• Demonstrated research experience in nanofabrication, sensing technologies, flexible electronics, bioelectronics, or a closely related area, as 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.

Preferred Qualifications

• Research experience involving plasmonic sensing, surface-enhanced Raman spectroscopy (SERS), optical sensing, bioelectronics, and 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.

Overtime Status

Exempt: Not eligible for overtime

Appointment Type

Restricted

Salary Information

65,000 - 75,000

Hours per week

40

Review Date

December 1, 2026

Additional Information

The successful candidate will be required to have a criminal conviction check.

About Virginia Tech

Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including Blacksburg, the greater Washington, D.C. area, the Health Sciences and Technology Campus in Roanoke, sites in Newport News and Richmond, and numerous Extension offices and research institutes. A leading global research institution, Virginia Tech conducts more than $650 million in research annually.

Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development.

Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.

If you are an individual with a disability and desire an accommodation, please contact Cole Tankersley at cpt19@vt.edu during regular business hours at least 10 business days prior to the event.