Design and operation of net-zero smart resilient buildings and infrastructure
Funded PhD position in Building Engineering
Last updated: August 4, 2025, 2:25 p.m.
Supervisory details
Supervisor: Mohamed Ouf
Department: Building, Civil, and Environmental Engineering, Gina Cody School of Engineering and Computer Science
University: Concordia University, Montreal, Canada
Start date: Flexible (Fall 2025 preferred)
PhD Fellowship: 35K CAD per year for 4 years
Project overview
This project aims to design and demonstrate integrated, carbon-neutral energy systems for buildings and infrastructure by combining on-site renewables, energy storage, advanced HVAC, and EV interaction. Emphasis is placed on architectural integration of PV technologies, smart grid operation, and resilience to extreme weather through innovative materials and scalable solutions.
Dr. Ouf is looking for strong PhD candidate(s) to work on developing data-driven techniques to improve both energy efficiency and occupant comfort in buildings, with specific topics focusing on:
- Advanced building control strategies (particularly occupant-centric controls (OCC), but also MPC, AFDD)
- Integration of renewables in building design (e.g., Building Integrated Photovoltaic (BIPV) / (Building Integrated Photovoltaic/Thermal (BIPV/T)
- Developing Grid-interactive operational strategies
Role description
- Develop grey-box RC (Resistance ‐ Capacitance) models of buildings and building clusters for model-predictive control (MPC), integrating technologies like smart windows, BIPV/T systems, thermal storage, and bidirectional EV-grid interactions.
- Design occupant-centric control (OCC) strategies that learn from and adapt to occupancy patterns and occupant feedback to improve energy efficiency, comfort, and EV charging.
- Implement low-order building energy models suitable for real-time optimization of smart grid interactions and energy flexibility within microgrids.
- Conduct case studies and technoeconomic analyses using archetype thermal models and machine learning to forecast energy loads, enhance HVAC control, and optimize demand response.
- Apply transfer learning techniques to adapt RC models across different building types, reducing the need for extensive simulations and enabling scalable, cost-effective energy management solutions.
The candidate(s) may work in collaboration with other partners in both industry and/or academia.
Research areas
- Occupant-Building interactions research
- Building Performance Optimization
- Occupant-Centric Building Controls
- Data-Mining and Machine Learning for Building Operations
- Net-Zero Energy Buildings
Requirements
- A recent MSc degree in an engineering discipline (Building, Mechanical, Architectural, Civil) or a related field
- Experience in modelling building energy systems, and using at least one building simulation tool (e.g. Energy Plus, eQUEST, IESVE)
- Strong background in statistics, data analytics and machine learning, as well as linear and non-linear optimization techniques
- Strong technical writing skills and interest in producing high quality publications in top journals and conferences Experience with programming languages (i.e., Python, MATLAB, etc.)
- Excellent English written and oral communication skills (French is considered an asset)
Please combine the following documents into a single PDF file.
- Detailed curriculum vitae (CV)
- Cover letter (1 Page, no more than 500 words) explaining your suitability for the position and outlining previous research, modelling and/or programming experience. Please indicate which topic you are more interested or have more experience in.
- Contact information of at least two professional or academic references.
- Transcripts and graduation certificates
If you are already in Canada, or if you’re a Canadian citizen or Permanent Resident, please highlight this in your communications.
Send your PDF file to volt-age.recruitment@concordia.ca with the subject Net-Zero Smart Resilient Buildings_Your name.
Applications will be considered on a rolling basis.
Questions/contact
For all questions, please contact Alisa Makusheva at alisa.makusheva@concordia.ca.
