Research FELLOW - Michigan Cognitive and Adaptive Manufacturing Lab

Who We Are

At Michigan Engineering, we develop the talent and technologies that move society forward and serve our state and national interests. Through discovery and innovation, we create the foundational knowledge and practical technologies to solve not only today's most pressing challenges, but also power industries and change lives. Our programs and community are designed to promote personal well-being and achievement - enabling everyone to unlock their potential and contribute with confidence.

Manufacturing is a highly complex field involving materials, machines, and processes that interact in ways we often don't fully understand. This complexity poses a significant challenge when we attempt to optimize manufacturing processes, introduce new materials, or enhance efficiency. While artificial intelligence (AI) has shown great promise in helping us identify patterns, make predictions, and drive innovation in manufacturing, understanding how some AI models make their predictions or decisions is very difficult.

This 'black box' problem is a significant challenge, especially when manufacturers need to trust AI tools to improve production, quality, and safety. The M-CAML (Michigan Cognitive and Adaptive Manufacturing Lab) team aims to develop methods and tools for interpreting and explaining how powerful AI models make predictions and decisions in manufacturing. By seeing what's going on inside the 'black box', we can also uncover new scientific or engineering insights about the underlying manufacturing processes.

The ability to interpret and explain AI models is called 'explainable AI' (XAI) and is an exciting and rapidly expanding research area. We will conduct a comprehensive study of AI models and architectures as well as methods, demonstrated, or projected, that are used to interpret mathematically different classes of AI models, and which we believe can be used in manufacturing contexts.

The outcome of this work will be a validated compilation of quantitative information that will serve to project opportunities as well as baseline data set for assessing these methods and models. The objective is to actualize M-CAML's research agenda that attracts partners and collaborators across sectors (industry, government, academia) and horizons (short, medium, long-term). This research agenda will also support research in cognitive manufacturing, generative design of manufacturing systems, and Digital Twin architectures.

We hope that you will consider becoming the newest member of the M-CAML team.

• Conduct a comprehensive literature review and extract relevant data;
• Design and execute novel research in related topic areas;
• Prepare manuscripts, proposals, and presentations;
• Guide student research assistants.

• PhD in an engineering discipline or a related field;
• Experience in collaborative research contexts;
• Strong demonstrated verbal and written communication skills.

• Familiarity with manufacturing systems and processes;
• Experience with machine learning and AI tools.

Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department.

Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.

The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks are performed in compliance with the Fair Credit Reporting Act.

Job openings are posted for a minimum of seven calendar days. The review and selection process may begin as early as the eighth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.

The University of Michigan is an Equal Opportunity Employer. We are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants, including protected veterans and individuals with disabilities.

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