Workforce Development for Future Smart Energy Systems

NSF CyberTraining Project: Secure, Resilient Cyber-Physical Energy System Workforce Pathways via Data-Centric, Hardware-in-the-Loop Training (2023 - 2027)

Florida Atlantic University leads the workforce development for future smart energy systems and prepares future STEM scientists and engineers. This is in collaboration with the University of South Florida, Florida International University, and the University of Central Florida.

Students and professionals are provided with mentored, hands-on training combining expertise across electrical engineering, communication, data science, and security. Participants develop and refine the multi-disciplinary skillsets needed for the data-centric power and energy industry using unique, remotely connected smart grid cyberinfrastructure. Participants extend their academic research portfolios, strengthening their career competitiveness as future cyberinfrastructure professionals and users. The project thus serves the national interest, as stated by NSF's mission: to promote the progress of science and to secure the national defense.

Our training program includes the following key areas:

  • Artificial Intelligence and Data Analytics
  • Communication, Network, and Hardware Security
  • Sensor Networks and Internet-of-Things
  • Renewable Energy Modeling and Simulation
  • Real-Time Learning and Microgrid Optimization
  • Multi-Level Decision-Making Process of Intelligent Systems
Infographic: Smart Grid

Smart Grid and Renewable Energy (Infographic description)

Infographic: Internet of Energy

Internet of Energy (Infographic description)

YOUR FLORIDA EXPERIENCE

  • This paid, nine-day program focuses on virtual/remote training and providing students and professionals with an exciting experience that will prepare them for research or careers in engineering and science.
  • This program welcomes professionals (e.g., power engineer or software engineer working in a related industry) to apply.
  • Participants will typically work 9 a.m. to 5 p.m. each day. Participants are paid a $700/week stipend (total $1,400).
  • Additional paid, project-based training opportunities are also available after the nine-day training. Please see the project descriptions below.

2021 Training Outcomes: 29 participants have been virtually trained for two weeks in the summer of 2021.

2021 Participants

2022 Training Outcomes: 44 participants have been virtually trained for two weeks in the summer of 2022.

2022 Participants

Project-based Training:

We plan to provide two additional longer-term research projects for the Summer/Fall of 2022. Participants are encouraged to take the two-week training to build the basic skills. Certain amount of incentives are also provided if participants are selected on the additional projects. The projects are described below.

Community Microgrid Energy Management
Led by Zhen Ni, Ph.D.

In-Person or Remote project
Learn more

Machine Learning Techniques to Detect GPS Spoofing Attacks
Led by Dongliang Duan, Ph.D.

In-Person or Remote project
Learn more

Project-based Training:

We intend to offer an extra four long-term research projects, which can be conducted either in-person or remotely, subsequent to the completion of the training. Participants are strongly urged to undertake the nine-day training to establish foundational skills. A specified level of incentives will be extended to selected participants who participate in these supplementary projects. Further details about the projects can be found below, and individuals interested in their availability are encouraged to reach out directly to the lead faculty mentor.

Spatial-Temporal Big Data Analytics for Cyber-Physical Energy System Cybersecurity
Led by Yufei Tang, Ph.D.

Reinforcement Learning for Resilient Micro-Gird Infrastructure
Led by Zhen Ni, Ph.D.

Adaptive Defense against Malicious Network Inference in Critical Infrastructures
Led by Zhuo Lu, Ph.D.

Graph Signal Learning for Situational Awareness under Graph Uncertainties
Led by Mahshid Naeini, Ph.D.


HOW TO APPLY

Submit the following information in one email to Sasha Fung. (cybertrainingfau@gmail.com).

  1. One-page statement of interest that describes your motivations, expectations, and long-term objectives.
  2. One letter of recommendation from your advisor/mentor/supervisor.
  3. A two-page comprehensive CV featuring essential personal details, including demographic information if available.
  4. Complete transcript.

DATES

Application Window Opens: March 1, 2024
Application Deadline: April 19, 2024
Notification Date: On or before May 3, 2024
Nine-day Training: May 20, 2024 – May 31, 2024 (excluding weekends and holidays)

ELIGIBILITY

  • Preference will be given to graduates and professionals in science or engineering. Exceptional senior undergraduates will also be considered.
  • Minorities and women are strongly encouraged to apply.
  • Selections will be based on a combination of research interests, academic qualifications, and faculty recommendations.

CONTACT

For additional information please contact Yufei Tang, Ph.D. at (561) 297-4781 and tangy@fau.edu.

To print the NSF CyberTraining Flyer, click here.

FUNDING

This program is currently funded by the National Science Foundation grants OAC 2320972/2320973/2320974/2320975. This program has been funded by the National Science Foundation grants OAC 2017597/2017194 & 1949921/1923983

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