Postdoctoral Associate - Instrumentation and Photonic Workflow Validation

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 dielectric meta-optics and, as project priorities permit, plasmonics and thin-film lithium niobate (TFLN) integrated photonics 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 develop and validate experimental workflows connecting photonic design and electromagnetic modeling with device fabrication, calibrated optical and structural metrology, instrumentation, synchronized data acquisition, uncertainty-aware model validation, and reproducible experiment records. The position will contribute to human-supervised, progressively programmable research workflows; quantitative fabrication-to-performance relationships; digital-twin validation; and machine-readable records linking designs, samples, processes, 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 scholarly publications, technical presentations, student mentoring under faculty supervision, professional-development planning, and proposal or fellowship participation as appropriate.

The position focuses on research instrumentation development, quantitative method validation, and integration of experimental data into reproducible PCL workflows. Routine preventive maintenance, service-center administration, instrument scheduling, and independent authorization of facility access are not primary responsibilities. All facility activities will be coordinated with authorized personnel.

Required Qualifications

• PhD in electrical engineering, optical science or engineering, materials science, physics, photonics, 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 meta-optics, nanophotonics, integrated photonics, photonic device fabrication, or a closely related area, as documented through doctoral research, publications, or research projects.
• Experience conducting fabrication or characterization experiments involving semiconductor, nanophotonic, or integrated photonic devices.
• Experience using laboratory instrumentation, optical characterization equipment, sensing systems, or metrology tools 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, 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 to coordinate with faculty mentors, researchers, and facility-authorized personnel.

Preferred Qualifications

• Research experience involving dielectric meta-optics, nanophotonics, thin-film lithium niobate (TFLN), or integrated photonic devices.
• Experience with electron-beam lithography (EBL), plasma etching, optical characterization, or related nanofabrication techniques.
• Experience with calibrated spectral, imaging, wavefront, coupling, propagation-loss, polarization, or device-response measurements.
• Experience with automated experimentation, instrument control, synchronized data acquisition, or process monitoring.
• Familiarity with digital twins, inverse design, optimization methods, or uncertainty quantification.
• Experience using Python, MATLAB, or comparable programming tools for scientific computing, instrument automation, or experimental data analysis.
• Experience with structured scientific metadata, experiment manifests, research-data provenance, FAIR or AI-ready data practices, or reproducible experimental/computational workflows.
• Experience mentoring students, developing SOPs or training materials, supporting research users, or contributing to proposals.
• Demonstrated experience collaborating with researchers across engineering, physics, materials science, or related disciplines.

Overtime Status

Exempt: Not eligible for overtime

Appointment Type

Restricted

Salary Information

65,000 - 75,000, Commensurate with Experience

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 communitydedicated 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 dedicated to knowledge, discovery, and creativity. 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 officesand 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.

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