Postdoctoral Associate

Tribonet; Blacksburg, Northern (VA, KY); Hybrid; USD 55,000 - 70,000; Full time

Job summary

Virginia Tech in Blacksburg seeks a postdoctoral researcher in the Department of Mechanical Engineering to advance uncertainty quantification and machine learning for LPBF process-structure-property relationships. You will characterize LPBF microstructure and surface topography and relate findings to process parameters.

The role includes mentoring graduate students, collaborating with research groups, and contributing to STEM outreach.

Qualifications

• PhD in mechanical engineering or related field.
• Evidence of high-quality research publications in relevant areas.
• Experience in metal additive manufacturing, tribology, or uncertainty quantification.

Responsibilities

• Characterize surface topography and microstructure of LPBF parts.
• Relate microstructure and surface data to LPBF process parameters using UQ framework.
• Collaborate with graduate students and mentors; contribute to STEM outreach.

Skills

Strong research publications

Experience in metal AM/tribology/unc.

Self-motivated

Effective communication skills

Education

PhD in mechanical engineering

Tools

EBSD measurements

DOE software (R/Python)

Job description

An educational platform on tribology — the science of friction, wear and lubrication

Who is it for

Engineers, researchers, students and industry professionals

Job Description

The Department of Mechanical Engineering at Virginia Tech invites applications for a funded post-doctoral researcher position, starting as soon as possible.

Using metal additive manufacturing (AM) as a production process instead of a prototyping tool requires reliably and repeatably manufacturing parts with near-identical microstructure, surface topography, and properties. Consequently, understanding the uncertainty associated with the process-structure-property-surface (PSPS) relationship is of crucial importance. Yet, PSPS research is time-consuming and costly because many specimens are required to derive meaningful information and, alternatively, aggregating existing datasets of different studies and research groups to expand and enhance insights about the PSPS relationship is not straightforward because of access/permissions and inconsistencies between data formats.

This research project aims to combine an uncertainty quantification (UQ) framework with machine learning (ML) algorithms to derive data-driven models that relate laser powder bed fusion (LPBF) process parameters to metrics that quantify the microstructure and as-built surface topography. The knowledge resulting from this research will (1) quantify the uncertainty of the microstructure and as-built surface topography as a function of the L-PBF process parameters; (2) determine the LPBF process parameters required to obtain specific uncertainty (or probability definition) of microstructure and as-built surface topography; (3) derive an operating map of the solution of the forward and inverse problems and its uncertainty as a function of the L-PBF process parameters; (4) implement a cloud-based database to aggregate microstructure images and surface topography maps that can be cited using a digital object identifier (DOI).

The individual filling this position will be expected to work on characterizing the surface topography and microstructure of LPBF parts and relating that knowledge to LPBF process parameters through a UQ framework. Additionally, the individual will participate in STEM outreach at Virginia Tech.

The project is sponsored by the National Science Foundation, and is led by Prof. B. Raeymaekers and Prof. Pinar Acar in the Department of Mechanical Engineering at Virginia Tech. The postdoctoral associate will join two vibrant research groups (Raeymaekers and Acar groups) with PhD, MS, and undergraduate students, and they will have the opportunity to collaborate with other lab members and contribute to mentoring graduate students.

The project is currently funded, so the start date is as soon as practical. The position will be located at Virginia Tech in Blacksburg, VA. Candidates should submit a curriculum vitae, publication list, statement of research, and list of three references.

Required Qualifications

• A PhD degree in mechanical engineering or other related scientific discipline. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining.
• Established track-record of high-quality research publications;
• Experience in either metal AM, tribology, or uncertainty quantification;
• Self-motivated, desire to learn, and go-getter personality;
• Demonstrated human relations and effective communication skills.

Preferred Qualifications

• Knowledge of design of experiments, statistical analysis, or machine learning;
• Experience with surface topography and/or electron backscatter diffraction (EBSD) measurements;
• Experience with metal AM, tribology, design of experiments, uncertainty quantification, statistics and/or machine learning

Appointment Type

Restricted

Review Date

October 2, 2023

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 the Innovation Campus in Northern Virginia; the Health Sciences and Technology Campus in Roanoke; sites in Newport News and Richmond; and numerous Extension offices and research centers . A leading global research institution, Virginia Tech conducts more than $500 million in research annually.

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, 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 Amanda Collins at [e-mail] during regular business hours at least 10 business days prior to the event.