Entrepreneurial Postdoc Position in Explainable AI for Predictive Maintenance (Closed — Stay tuned for future openings)

Applications are invited for one entrepreneurial postdoctoral research position in the area of deep learning and explainable artificial intelligence (AI) for the Industrial Internet-of-Things (IIoT). This position is joint between the University of Connecticut and Perc?v (an IIoT start-up and a subsidiary of Grace Technologies). The postdoc will be co-advised by Dr. Chao Hu, an Associate Professor of Mechanical Engineering at the University of Connecticut, and Dr. Andy Zimmerman, the CTO of Grace Technologies and the CTO and co-founder of Perc?v. A successful applicant will play a central role in an NSF-funded research project on the design, development, and field validation of probabilistic and explainable deep learning methods for the predictive maintenance of industrial and agricultural equipment.

One unique attribute of this position is that this postdoctoral research associate will be deeply engaged in the process of translating this novel research into a commercially viable business opportunity. This process encompasses many elements essential to becoming a young entrepreneur, including field validation (working with 3+ potential customers), product design, IP portfolio design and protection, fundraising, and product/market analysis. The successful applicant will lead interactions between the University of Connecticut, Perc?v, and outside industrial and commercial partners including early-stage customers. Individuals who strongly desire to learn entrepreneurial skills or pursue an entrepreneurial career are encouraged to apply; however, prior experience in entrepreneurship is not required.

The duration of the position is expected to be one year and may be renewable based on performance and funding availability. The salary will be commensurate with the prior research experience of the applicant.

Qualifications:
Candidates should have a recent Ph.D. in Data Science, Software Engineering, Mechanical Engineering, Industrial Engineering,  Electrical Engineering, or a related discipline. Prior research experience in one or more of the following areas is desired:

– Algorithm development for machine learning and deep learning
– Hardware/software/firmware development for IoT devices
– Full-stack software development
– Implementation of deep learning algorithms in an embedded computing environment
– Algorithm development for fault diagnostics and failure prognostics
– Vibration analysis for diagnostics/prognostics of rotating machinery

Responsibilities:
The postdoc is expected to spend 50% of their time on technology development at the University of Connecticut and the other 50% supporting and leading commercialization activities at Perc?v. The postdoc will attend the weekly individual and project team meetings, mentor at least two Ph.D. students at the University of Connecticut, and facilitate close collaborations with other universities and companies interested in explainable deep learning and low-cost, scalable IIoT platforms. The postdoc will publish their research findings in premier journals, present their research in high-impact conferences, and participate in proposal writing.

Applicant Information:
The position is available immediately. Applications will be processed as they arrive until the position is closed. Interested applicants should submit by email (1) a cover letter that summarizes prior research experience and (2) a CV to Dr. Chao Hu, chao.hu@uconn.edu, and Dr. Andy Zimmerman, andyz@percev.co.

Open URA Positions in ML for Diagnostics and Prognostics
— We don’t have any openings for PhD students at this time. Stay tuned for future openings!

Our group has projects on topics related to real-time machine learning (ML) and health prognostics – we are trying to answer the questions (1) “how fast can we detect and quantify an event?” with applications to blast detection and other fun systems like hypersonic vehicles and (2) “how early can we predict battery end-of-life with confidence?” with applications to electric vehicles (EVs) and grid storage. You will learn how to integrate ML with sensor measurements through these projects. Wait, hypersonic vehicles? EVs? ML? What do we know about this? A lot! Chao can convince you that you know enough about it to do the research with a Mechanical Engineering background! Graduate research is easier than you might think, especially if you are self-driven and intellectually curious. On the technical side, you need to be strong in mathematics and statistics and willing to learn to program in MATLAB/Python.

Want to know more? Check out this website: https://chaohulab.engr.uconn.edu/ and email Chao Hu: chao.hu@uconn.edu.