Position Purpose:
The Energy and Power Systems research group at the Electrical Engineering Department, LUMS, is seeking highly motivated individuals to join our research team as a full-time Research Assistant to develop next-generation artificial intelligence (AI) and optimisation algorithms for real-time congestion management in distribution feeders to enhance renewable energy integration. The successful candidate will be involved in a research project that focuses on either deep reinforcement learning or optimisation-based operation of smart distribution grids.
Main Responsibilities:
- Develop deep reinforcement learning or optimisation algorithms for real-time control of smart grids
- Implement AI-based models using PyTorch, StableBaselines3, and RLlib
- Formulate optimisation-based models using GAMS, YALMIP or Pyomo
- Perform power system simulations using OpenDSS or DigSILENT PowerFactory
- Benchmark the performance of AI and optimisation approaches using technical, economic, and environmental performance metrics
- Contribute to the preparation of research publications, project reports, and funding proposals
- The candidate should possess a BS degree (MS degree preferred) in Electrical Engineering (EE). The ideal EE candidate will have a focus on Power Systems with expertise either in Artificial Intelligence (Deep Learning and Reinforcement Learning) or Optimisation (Mathematical and Metaheuristic).
Skills and Attributes:
- Experience in applying AI techniques in Python, including PyTorch, StableBaselines3, and RLlib
- Experience in mathematical optimisation with software tools like GAMS, AMPL, YALMIP or Pyomo
- Hands-on skills in power system simulations using OpenDSS or DigSILENT PowerFactory
- Documented (publications) experience in the application of AI or optimisation in power systems
- Great command of written and verbal communication skills
- Excellent analytical, coding, and problem-solving skills
Please send your updated CV and BS/MS transcripts to Dr. Raheel Zafar, Assistant Professor (raheel.zafar@lums.edu.pk), along with a cover letter clearly highlighting your background relevance to the advertised position. Please use ‘Research Assistant – Real-Time Congestion Management’ in the subject of the email.
Application Deadline: August 31, 2026 (Applications will be considered on a rolling basis