Research Associate in Mechanical Engineering – Monitoring and Computer Vision

Research Associate in Mechanical Engineering – Condition Monitoring and Computer Vision

Job No: 529241
Position type: Staff Full Time
Benefit Status: Benefited-Union
Campus: UMass Lowell
Department: Mechanical & Industrial Engin
Salary: Anticipate salary up to $70,000 commensurate with grade/range
Applications Open: Dec 18 2025
Applications Close: Open until filled

The successful Research Associate will conduct analytical and experimental research in nondestructive evaluation and condition monitoring with emphasis on computer vision and machine learning-enhanced techniques for damage detection, data acquisition, and system monitoring for energy systems (e.g., wind, solar, hydrogen). The candidate will assist in writing scholarly articles and research proposals, manage and educate graduate or undergraduate students, and support laboratories and facilities.

The initial appointment will be for 1 year with possibility of renewal based on productivity, performance, and funding availability.

• Conduct analytical and experimental research under the supervision of the Principal Investigator (PI).
• Acquire data of targeted structures using unmanned aerial vehicles embedding different sensors such as RGB, RGB_D, infrared cameras, LiDAR scanners, etc.
• Develop appropriate image processing algorithms and approaches to analyze the collected datasets.
• Conduct research related to environmental conditions and operations of installed energy storage and power generation systems.
• Develop a supervisory control and data acquisition (SCADA) system for monitoring and control of energy systems.
• Develop appropriate machine learning algorithms and models to automate the detection of damage in collected datasets.
• Ensure all project-related activities are conducted on time and to the highest standard.
• Prepare status reports and deliverables in collaboration with the PI.
• Serve as a liaison between the PI and collaborating research partners.
• Assist the supervisor in writing scholarly articles and grant proposals.
• Publish at least two articles per year in peer‑reviewed scientific journals; present at conferences; development of patentable technologies encouraged.
• Initiate and assist in formulation and writing of externally funded research proposals.
• Assist in preparing programmatic reports and making presentations to sponsors/funding agencies.
• Attend meetings related to the research projects.
• Conduct weekly meetings between PI, Co‑PI, and other group members.
• Disseminate knowledge by attending technical meetings and conferences nationally and internationally when required.
• Supervise undergraduate and graduate students.
• Meet weekly with each student to review research progress.
• Provide mentorship or training to graduate and undergraduate students.

• Promote the University’s commitment to customer service.
• Build effective partnerships with co-workers throughout the University.
• Ensure optimum service to all internal and external partners in response to all requests for service and information.
• Maintain a welcoming environment for all backgrounds, nationalities, and roles.
• Help with the general operation of the lab and Center for Energy Innovation.
• Perform other related duties as assigned.
• Facilitate occasional travel that may involve overnight or day travel, nationally and internationally.

Minimum Qualifications:

MS or PhD in Mechanical Engineering, Industrial/Systems Engineering, Civil Engineering, Electrical Engineering, Computer Engineering/Science, Physics, Applied Physics, Chemistry, or related scientific degree.

Minimum three years of experience (including MS/PhD research) in research related to structural health monitoring, image processing, data acquisition, signal processing, and conducting analytical research, experimental planning, and testing in laboratory environments.

• Ability to work as a team player.
• Outstanding technical report writing and communication skills.
• Experience developing computer algorithms and models for monitoring and control of mechanical, electrical, and energy systems, and computer vision and machine learning algorithms for structural health…