The Ira A. Fulton Schools of Engineering at Arizona State University (ASU) and the School for Engineering of Matter, Transport and Energy seek applicants for appointments made at the tenure-track or tenured faculty position in the area of machine learning in mechanical and materials engineering. The School for Engineering of Matter, Transport and Energy, one of the seven Fulton Schools, houses a vibrant community of researchers in the mechanical and aerospace engineering, chemical engineering, and materials science disciplines. We seek applicants who complement our abilities in these areas and will discover new Machine Learning/Artificial Intelligence-based approaches for advancing the fields of mechanics and materials engineering. Areas of interest include but are not limited to, aerospace materials and structures, materials characterization and/or processing/manufacturing, mechanics of defects, materials reliability and failure, and computational materials science.
We seek applicants who will contribute to our academic programs, promote transdisciplinary teaching and research, and help the University to achieve its aspirations, including enabling student success, transforming society, valuing entrepreneurship and conducting use-inspired research. Faculty members in the Fulton Schools are expected to develop an internationally recognized and externally funded research program, adopt effective pedagogical practices in the development and delivery of graduate and undergraduate courses, advise both undergraduate and graduate student research and projects and undertake service activities.
Minimum Qualifications:
- Earned doctorate degree in aerospace engineering, chemical engineering, materials science, mechanical engineering, or related discipline at the time of appointment, and
- Evidence of excellence in research, as appropriate to the candidate’s rank, and
- Evidence of excellence in teaching, as appropriate to the candidate’s rank.
Preferred Qualifications:
- Evidence of expertise and/or sophisticated research plans for applying machine learning and artificial intelligence to solve mechanical/materials engineering problems, and
- Demonstrated commitment to a collaborative, transdisciplinary approach to research and teaching, and
- A record of acquiring external funding, as appropriate to the candidate’s rank, and
- A record of impactful publications in top-tier journals/conferences, as appropriate to the candidate’s rank, and
- Commitment to mentoring students and delivering courses in multiple technology-enhanced formats, as appropriate to the candidate’s rank, and
- Commitment to curriculum development and innovative pedagogy; and a demonstrated appreciation for entrepreneurial activities, as appropriate to the candidate’s rank.
- Demonstrated impact to diversity and inclusion for broadening participation in science and engineering.