About Us - Values Overview

Department Summary

CAAAM is the Center for Agile and Adaptive Additive Manufacturing. CAAAM looks at additive manufacturing, which includes materials, science, cybersecurity, data analytics, logistics, and supply chain.

Established in 2019 by the Texas State Legislature with $10 million in funding and recently renewed through 2025, CAAAM conducts next-generation research, education, and workforce training in additive manufacturing. CAAAM's Institute for Transformative Education in Additive Manufacturing-or ITEAMprovides courses, seminars, workshops, symposia, in-lab training sessions, and summer camps for industry and community colleges.

UNT is rapidly expanding its Center for Agile and Adaptive Manufacturing (CAAAM) (caaam.unt.edu), a State of Texas funded multi-million-dollar initiative with a multi disciplinary focus on further advancing the science and technology of additive manufacturing (AM). CAAAM involves a multi-disciplinary team of researchers from materials science, mechanical engineering, manufacturing, data science, cybersecurity and logistics & supply chain, committed to collaborating on large research projects with an emphasis on additive manufacturing. One of the focus areas of CAAAM is Data Science/Analytics (Machine Learning, ML/Artificial Intelligence, Al) in the context of advanced manufacturing systems in general and additive manufacturing systems more specifically.

UNT has a highly diverse campus with a wide range of languages spoken in addition to English. We welcome candidates who have experience with HSI/MSIs and/or who speak Spanish, Vietnamese, American Sign Language, Chinese (Cantonese, Mandarin and other variations), Arabic, Tagalog, Farsi, French, or/and Yoruba.

Position Overview

This PostDoctorate Research Associate (PDRA) role is primarily focused on the independent technical delivery of and leadership in the development of projects/activities related to ML/ AI in AM within CAAAM The PDRA will work with the faculty across various departments with interdisciplinary approach to AM The primary responsibility of this position is to develop digital manufacturing capabilities using ML/ AI learning approaches in AM.

The PDRA will provide solution concepts and develop, integrate and implement data driven approaches (experimental dataset physics based model dataset) to achieve process optimization, defect prediction and property prediction in various AM processes. The additional specific capabilities highly desired of the candidate include but not limited to: experience in ML/ AI approaches, application of ML/AL in image processing, reasonable understanding of science/physics and engineering of AM processes for metallic systems, knowledge and experience in heat transfer/fluid dynamics/structural mechanics through the applications of computational and statistical methods.

The position is also expected to provide technical expertise and support to graduate and undergraduate students, assist/lead preparation of research proposals and scientific/technical papers and presentations.

Minimum Qualifications

Earned doctorate in mechanical engineering or materials science and engineering or physics or a related discipline with a research focus on ML/ AI in materials processing/manufacturing.

Record of publications related to Machine Learning/Artificial Intelligence for Additive Manufacturing.

Preferred Qualifications

Applicants that demonstrate aptitude and experience in interdisciplinary collaborations will be a plus.

Required License/Registration/Certifications

Job Duties

• Conduct experiments, gather data, document research notes, code and analyze data, interpret data.
• Prepare manuscripts for publications in a timely manner and talks and posters for presentation at group meetings and conferences. Assist with reporting of research progress to funding agencies. 
• Keep up with historical and latest research data and publications.
• Stay up-to-date with all trainings, and federal, state, university and department requirements. 

Physical Requirements

Communicating with others to exchange information.

Lifting and Moving objects up to 20 pounds.

Environmental Hazards

Small and/or enclosed spaces.

Work Schedule

Varies based upon assignment

Driving University Vehicle

No

Security Sensitive

This is a Security Sensitive Position.

Special Instructions

Applicants must submit a minimum of two professional references as part of their application. If needed, additional references can be added after the application has been submitted. 

Benefits

For information regarding our Benefits, click here.

EEO Statement

The University of North Texas System is firmly committed to equal opportunity and does not permit –and takes actions to prevent – discrimination, harassment (including sexual violence, domestic violence, dating violence and stalking), and retaliation on the basis of race, color, religion, national origin, sex, age, disability, genetic information, or veteran status in its application, employment practices, and facilities; nor permits race, color, national origin, religion, age, disability, veteran status, or sex discrimination and harassment in its admissions processes, and educational programs and activities. UNT System Administration promptly investigates complaints of discrimination, harassment, and related retaliation and takes remedial action when appropriate. System Administration also takes actions to prevent retaliation against individuals who oppose any form of harassment or discriminatory practice, file a charge or report, or testify, assist, or participate in a related investigation or proceeding.