Metals Research and Applications Laboratory
Research Field
Professor E-Wen Huang is a professor and Chair of Materials & Science Engineering Department at the National Yang Ming Chiao Tung University, Taiwan. His research focuses on the mechanical behavior of the 3D additive manufacturing metals and high entropy alloys. To discover underneath mechanisms, he teams up with National Synchrotron Radiation Research Center and National Center for High-Performance Computing to combine in-situ measurements and multi-scale model fitting/simulation. Here, we set up a digitalization environment of materials science and engineering (MSE) for research, design, and optimization by computer-driven simulations, artificial intelligence, and machine learning. The For more details, please visit the website of (https://www.researchgate.net/profile/E-Wen-Huang).
Please visit our website at (https://www.researchgate.net/profile/E-Wen-Huang) for the research. For team members' activity and introduction, please visit (https://ewen-lab.wixsite.com/ewenlab).
Materials Science, X-ray, Characterizations, Metals, Addtive Manufacturing, Machine Learning, Metadata for findable, accessible, interoperable, and reusable (FAIR) principles.
Senior Fellow of the Higher Education Academy (SFHEA)
Distinguished Mentor Award, National Chiao Tung University
Ta-You Wu Memorial Award
Innovation in Taiwan Award (國家新創獎), the Institute for Biotechnology and Medicine Industry (IBMI) and Research Center for Biotechnology and Medicine Policy (RBMP)
Elected to be Chairman of the National Synchrotron Radiation Research Center Users’ Executive Committee
Young Leaders Professional Development Award, Structural Materials Division, the Minerals, Metals & Materials Society (TMS)
Representative of the TMS for the Emerging Leaders Alliance Conference
Honorable Mention of Service-Learning Pilot Program Contest, College Division, the Youth Development Administration, Ministry of Education
Ludo Frevel Crystallography Scholarship Award, The International Centre for Diffraction Data (ICDD)
Extraordinary Professional Promise of Chancellor’s Honors, The University of Tennessee Citation Award
The Phi Tau Phi Scholastic Honor
PhD, University of Tennessee, Knoxville, TN, USA
Master, Texas A&M University, College Station, TX, USA
Job Description
Responsibilities
Operate XRD instruments under supervision, including sample preparation, measurement setup, and routine maintenance logs.
Collect, document, and organize XRD data following FAIR (Findable, Accessible, Interoperable, Reusable) data principles.
Develop and self‑study ML workflows (e.g., using Python libraries) for XRD pattern preprocessing, feature extraction, and pattern recognition.
Participate in the design and implementation of data pipelines for automated or semi‑automated XRD data processing.
Collaborate creatively with group members, share ideas and codes, and contribute to joint research outputs (such as figures, reports, or draft manuscripts).
Take responsibility for research conduct, including proper documentation, data backup, and adherence to lab safety and research integrity guidelines.
Preferred Intern Educational Level
Required qualifications
Current undergraduate or graduate student in materials science, physics, chemistry, data science, or a related engineering/science field.oge.tmu+1
Strong respect for knowledge and genuine interest in developing science and technology as a long‑term career path.
Demonstrated ability for independent learning, especially in ML (for example, having completed online courses, self‑initiated projects, or GitHub portfolios).
Basic programming skills (preferably Python) and familiarity with fundamental ML concepts.
Good written and spoken English for technical communication and documentation.
Skill sets or Qualities
Preferred qualities
Spirit of creative collaboration: open‑minded, proactive in discussion, and willing to support teammates.oge.
High sense of responsibility for one’s own actions in the laboratory, including careful handling of instruments and data.
Prior experience with XRD, other characterization techniques, or scientific data management is a plus, but not mandatory.