Post - Doctoral Fellow, Engineering Department

External Applicants

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Job Description Summary

The Engineering Department at Wake Forest University is inviting applications for a postdoctoral research fellowship. The appointed candidate will collaborate closely with Dr. Hussein Abdeltawab to develop innovative solutions in forecasting and control systems pertaining to energy storage and renewable energy sources. We particularly welcome applicants possessing a strong foundation in power systems, power electronics, deep learning, optimization, and control systems applications. The successful candidate will participate in preparing presentations and scholarly articles for publication in high-tier journals. Additionally, the candidate will assist in mentoring research assistants and supporting the preparation of technical proposals.

This position is available on a one-year contractual basis, with the possibility of extension.

Essential Functions

• Technical algorithms utilizing advanced deep learning techniques for different power and energy applications.
• Writes project reports, journal articles, conference papers, and presentations.
• Build experimental test systems and simulators to collect data to validate modeling and analysis work.
• Support in technical proposal preparation for developing new projects.
• Contributes to the training of undergraduate and graduate research students.

Required Education, Knowledge, Skills, Abilities

• PhD or All but Dissertation (ABD) in Electrical and Computer Engineering or other related engineering disciplines.
• Strong publication record in the area of machine learning and deep reinforcement learning, with applications related to power systems.
• Demonstrated skill in developing code for machine learning tools using MATLAB, Python (TensorFlow, Porch), or similar tools.
• Demonstrated skill in building a simulation environment using MATLAB (MAT power), Python (Panda Power), or similar tools.
• Profound knowledge in power systems modeling, machine learning algorithms, control systems applications, and optimization.
• Proven track record of publications in Q1 journals.
• Outstanding skills in interpersonal communication and effective time management.
• Capability to work independently or in a cooperative team setting.

Preferred Education, Knowledge, Skills, Abilities

• Experience with physically informed machine learning (PNNLs) algorithms, auto-encoders, graphical neural networks (GNNs), and similar architectures.
• Proficiency with building Agentic AI, LLMs, and transformer algorithms.
• Experience in building hardware-in-the-loop systems, microgrids, or EV simulators.

Accountabilities

• Responsible for own work

Physical Requirements

• Moderate physical activity: Mainly working on computer algorithms.

Environmental Conditions

• No concerns or hazardous conditions in this job

This position is not eligible for sponsorship of non-immigrant or immigrant visa status through Wake Forest University. All eligible applicants are encouraged to apply.

Additional Job Description

Time Type Requirement

Full TimeNote To Applicant

This position profile identifies the key responsibilities and expectations for performance. It cannot encompass all specific job tasks that an employee may be required to perform. Employees are required to follow any other job-related instructions and perform job-related duties as may be reasonably assigned by his/her supervisor.

In order to provide a safe and productive learning and living community, Wake Forest University conducts background investigations and drug screens for all final staff candidates being considered for employment.

Equal Opportunity Statement

The University is an equal opportunity employer and welcomes all qualified candidates to apply without regard to race, color, religion, national origin, sex, age, sexual orientation, gender identity and expression, genetic information, disability and military or veteran status.

Accommodations for Applicants

If you are an individual with a disability and need an accommodation to participate in the application or interview process, please contact [email protected] or (336) 758-4700.