Funded PhD Positions in Sensing, Control, and Data-assimilation for Smart Buildings in Vancouver, Canada

• Digital Twins for Smart Buildings: Meeting energy efficiency regulations requires robust tools for monitoring and auditing the operational energy consumption of buildings. Our goal is to create energy consumption focused digital twins of smart buildings to enable real-time monitoring, analytics, and control. We will use tools from statistical filtering, uncertainty quantification, machine learning, to develop a digital twin that uses real-time sensor data to maintain an up-to-date model of a smart building and use this model to monitor efficiency, detect and isolate faults, perform predictive maintenance, and enable real-time optimization.
• Optimal control of indoor environment quality: Indoor environment quality has an impact on the overall health and well-being of building occupants. We aim to achieve optimal control of building systems (e.g., HVAC, lighting systems) to ensure occupant comfort while conserving energy and reducing greenhouse gas emissions. We will use non-intrusive sensing (fixed or mounted on mobile robots) to measure building system operation and occupants’ physiological response when exposed to changing indoor environment quality scenarios. Using the sensor data, we will use learning-based control to optimize building system operations and achieve a balance between occupant comfort and energy consumption in buildings.

These projects have theoretical, computational, and applied aspects that can be investigated in different ratios according to the interests of the researcher. Researchers will gain experience in control systems, optimization, machine learning, statistical data analysis, software development, ubiquitous sensing, human-participant experiments, and robotics.

About the Labs

These positions are split between the Algorithms Optimization and Control Lab (AOCL) and Intelligent Construction Lab (ICON) at the University of British Columbia in Vancouver, Canada.

• The AOCL works broadly at the intersection of control, optimization, and computing. We apply control, optimization, game-theory and machine learning to solve problems in energy, manufacturing, robotics, and aerospace.
• The ICON lab aims to understand how intelligent agents, such as construction robots and building control systems, interact with human operators, occupants, the built environment, and the society. We leverage machine learning and control theory to design, build, and control these intelligent agents, and conducts human participant experiments to study their impacts on the end users.

We are hosted within the Departments of Mechanical and Civil Engineering on UBCs Point Grey campus on the unceded lands of the xʷməθkʷəy̓əm (Musqueam) people, surrounded by forest, ocean and mountains. Vancouver is consistently ranked as one of the most diverse cities in Canada, and one of the most livable cities in the world. The lab offers a friendly and stimulating environment and the opportunity to become an expert in control, optimization, and computing while solving meaningful engineering problems.

Qualifications

• A Bachelor’s or Master’s degree in engineering (or a related field)
• Strong interpersonal and communication skills
• Some introductory undergraduate coursework on control, building science, optimization, machine learning, or robotics.
• No other specific qualifications beyond and a willingness to learn.

Organisation/Company – The University of British Columbia (UBC)

Research Field – Engineering » Civil engineering / Engineering » Control engineering / Engineering » Civil engineering / Engineering » Control engineering

Researcher Profile – First Stage Researcher (R1)

Country – Canada

Application Deadline – 15 Jun 2024 – 22:00 (UTC)

More information: EURAXESS