Assistant/Tenure-Track Professor in Mechanical Engineering: Structural Dynamics and Machine Learning

Assistant/Tenure-Track Professor in Mechanical Engineering: Structural Dynamics and Machine Learning

University of Massachusetts Lowell

Job no: 511465
Position type: Faculty Full Time
Benefit Status: Benefited-Union
Campus: UMass Lowell
Department: Mechanical Engineering
Applications Open: Dec 21 2021
Applications Close: Open until filled

 

General Summary of Position:

The Francis College of Engineering is searching for an outstanding candidate for a tenure-track Assistant Professor in Mechanical Engineering. The college includes 130 faculty members, over 30% of whom are women, and has over 3,000 undergraduate and 1,000 graduate students. The University is located in the historic industrial city of Lowell, Massachusetts and serves the multicultural population of the Commonwealth. The College has been nationally recognized for its commitment to diversity by the American Society for Engineering Education.

UMass Lowell is a Carnegie Doctoral High Research (RU/H) university ranked in the top tier of US News' National Universities and is strategically located 30 miles northwest of Boston in the northeast Massachusetts high-tech region.

The successful applicant will have opportunities to collaborate in research and teaching with faculty across our six departments. We value excellence and innovation in curriculum design and courses that promote experiential learning and professional skills for our students.

Minimum Qualifications (Required): 

  • Applicants must have earned a doctoral degree in Mechanical Engineering or a closely related discipline.

For these tenure-track positions in Mechanical Engineering (ME), we seek candidates with expertise in:

Structural Dynamics and Machine Learning: (a) structural dynamic modeling applications (reduced-order modeling, model updating, and system modeling including linear/nonlinear systems); (b) model calibration, validation, and uncertainty in structural systems; (c) structural health monitoring, smart structural systems and damage detection using artificial intelligence or machine learning. 

Candidates with experience in engineering education and the design of effective and inclusive pedagogical approaches will be given strong consideration.

The University of Massachusetts Lowell is committed to increasing diversity in its faculty, staff, and student populations, as well as in curriculum and support programs, while promoting an inclusive and nurturing environment. We seek candidates who can contribute to this goal and encourage candidates to apply and to identify their strengths in these areas. All applicants should include a statement of their efforts and vision on promoting diversity, inclusion, and women and minorities in engineering, and a statement of their teaching philosophy. 

Special Instructions to Applicants:

Applications received by March 1, 2022 will be considered in the first review of candidates.  However, later applications may be considered for these positions.  The position will close after an adequate number of qualified applications are received.

Please include the following documents with your application:

  • CV   
  • Cover Letter
  • Teaching Statement/Philosophy
  • Research Statement
  • Diversity Statement (Should include efforts and vision on promoting diversity, inclusion, and women and minorities in engineering).
  • Names and contact information of three references will be required during the application process.
  • Apply at: https://careers.pageuppeople.com/822/lowell/en-us/job/511465/assistanttenuretrack-professor-in-mechanical-engineering-structural-dynamics-and-machine-learning

 

The University of Massachusetts Lowell is an Equal Opportunity/Affirmative Action, Title IX employer. All qualified applicants will receive consideration for employment without regard to race, sex, color, religion, national origin, ancestry, age over 40, protected veteran status, disability, sexual orientation, gender identity/expression, marital status, or other protected class.