Postdoc: ML-Driven Mechanics for Recyclable Composites

The Chronicle Of Higher Education, Inc.; Houston (TX); On-site; USD 52,000 - 68,000; Full time

Job summary

The University of Houston invites applications for a postdoctoral position in the Department of Mechanical and Aerospace Engineering. The project focuses on developing machine learning–based approaches to predict structure–property relationships, including damage evolution, in recyclable composite materials.

The successful candidate will work at the interface of computational mechanics, materials science, and machine learning to create next-generation predictive models.

Qualifications

• PhD in Mechanical Engineering or a closely related field.
• Strong background in solid mechanics and computational micromechanics of materials.
• Experience with machine learning methods and tools.
• CV required along with references.

Responsibilities

• Receives, reviews progress and evaluates results of experiments or projects under supervision.
• Recommends changes in research, testing or experimental procedures.
• May be responsible for a single highly technical and complex piece of research equipment.
• May review and evaluate the effectiveness of personnel.
• May plan for and assign personnel to projects under control.
• Operates with latitude for unreviewed action.
• Performs other job-related duties as required.

Skills

Machine learning methods

Solid mechanics

Computational micromechanics

Research communication

Education

PhD in Mechanical Engineering or closely related field

Tools

ML frameworks

Job description

The University of Houston invites applications for a postdoctoral position in the Department of Mechanical and Aerospace Engineering. The project focuses on developing machine learning–based approaches to predict structure–property relationships, including damage evolution, in recyclable composite materials.

The successful candidate will work at the interface of computational mechanics, materials science, and machine learning to create next-generation predictive models.