Post Doc Research Fellow Lab

The postdoctoral fellow will play a key role in advancing next-generation digital twin technology that integrates real-time monitoring, simulation, decision-making, and structural control. Position is 100% funded by a sponsored research grant and is initially for a one-year term. Any renewal is contingent upon funding availability, project outcome and mutual interest. Teng Wu at the University at Buffalo.

The postdoctoral fellow will play a key role in advancing next-generation digital twin technology that integrates real-time monitoring, simulation, decision-making, and structural control. Position is 100% funded by a sponsored research grant and is initially for a one-year term. Any renewal is contingent upon funding availability, project outcome and mutual interest. Teng Wu at the University at Buffalo.

Description of the Work Unit
The CEE Department at CSU is recognized both nationally and internationally for its research, education, and service and outreach programs and is ranked in the top 40 CEE programs in the USA. The CEE Department comprises 34 faculty, 7 research scientists/scholars, 15 research associates, and 10 administrative professionals. Our water-related programs are designated a CSU Program of Research and Scholarly Excellence. Develop digital twin models for single-axis solar tracker arrays, integrating real-time monitoring, simulation, and structural control capabilities.

Implement and field-deploy digital twin platforms, ensuring reliable performance under real-world operating conditions.

Collaborate with the project PI and research partners at the National Laboratory of Rockies, University at Buffalo to plan and execute research activities that meet project milestones and deliverables.

Co-supervise and mentor graduate and undergraduate research assistants involved in the project.

Prepare manuscripts, technical reports, and conference presentations to disseminate research findings.

the Department will not sponsor a visa for this position.

Supervision

Will help co-supervise graduate and undergraduate research assistants involved in the project (quantity to be determined).

D. in a relevant field (e.g., Strong background in structural dynamics, wind engineering, structural control, health monitoring methods, and digital twin modeling.

Experience with machine learning and artificial intelligence methods.

A commendable record of research publications in relevant fields.

Research experience in uncertainty quantification.

Practical experience in software development and field implementation of digital twin.

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