Postdoctoral Appointee: Predictive Analytics & Optimization for Asset Management and Operations

Center for Energy, Environmental, and Economic Systems Analysis (CEEESA) works on innovative research to enhance the resilience, efficiency, and sustainability of power grid. Analytics on large-scale industrial data is unlocking emerging methods for operations, maintenance, and asset management. CEEESA is seeking talented and motivated researchers to enhance its capability in solving energy challenges using machine learning and data analytics.

The postdoctoral appointee will work with a team of researchers on leveraging data analytics to solve challenging problems in energy sector, such as data-driven asset diagnostics/prognostics, optimization of operations, maintenance, and related logistics. The researcher will perform theoretical study and algorithm development that leverages data analytics for (i) predictive methods in data-driven reliability assessment/degradation modeling, and (ii) develop computational methods and uncertainty models for solving energy optimization problems and publish in peer-reviewed journal/conference publications. The postdoctoral researcher will develop asset management tools that use large-scale industrial data, build ML/optimization software packages and help disseminate research results to academic and industry community, draft research proposals and apply for funding from federal agencies (e.g., the Department of Energy and National Science Foundation), and perform other tasks required for this position.

Position Requirements

• A PhD in Industrial Engineering, Electrical Engineering, Operations Research, Applied Mathematics, Computer Science, or other relevant domains.

• Knowledge and independent research capability in data analytics methods for asset diagnostics/prognostics and large-scale optimization with track records of publications.

• Experience and proficiency in developing data analytics packages with mainstream programming languages such as Julia, Python, Java, C/C++, etc.

• Proficiency and willingness to play an active role in scientific writing (e.g., publications and proposals), presentation at academic conferences, and other dissemination activities.

• Capability to communicate effectively with academic and industrial partners; ability to work in collaborative projects with team members of diverse backgrounds.

• A successful candidate must have the ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.

A successful candidate will have a solid background in predictive analytics and machine learning techniques, a track record of publications in machine learning conferences and predictive analytics journals, a highly skilled software development capability, prior experience in dealing with large-scale industrial datasets, and proposal writing experience.

Preferred Qualifications:

• Prior work on degradation modeling, and sensor-driven diagnostics / prognostics.

• Experience in machine learning and optimization.

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