Funded MASc Positions in Sensing, Control, and Data-assimilation for Smart Buildings

We have up to 4 funded MASc/PhD positions available on a variety of topics related to improving the energy efficiency and comfort of smart buildings. Buildings are responsible for 30% of worldwide greenhouse gas emissions and rapidly decarbonizing the building sector is essential for mitigating the worst impacts of climate change.

  • 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.

If you have any questions

If you’re interested in pursuing graduate school in beautiful Vancouver and any of this seems interesting to you, please don't hesitate to reach out.

How to apply

  • Submit the following documents by email at dliaomcp {at} mech.ubc.ca and zhengbo {at} civil.ubc.ca
    1. A resume/CV;
    2. A cover letter/statement of intent (including relevant experience, your research interests, and how they relate to these projects);
    3. Unofficial transcripts;
  • Please submit all documents as a single .pdf file and use the following format for the email subject line: [AOCL/ICON application]: LastName, FirstName
  • Those shortlisted for further consideration will be contacted for an online interview and given instructions about submitting a formal application.
  • Due to the volume of emails received only those shortlisted will be contacted; we apologize in advance.

We encourage applications from members of groups that have been historically disadvantaged and marginalized, including First Nations, Métis and Inuit peoples, Indigenous peoples of North America, Black-identified persons, other racialized persons, persons with disabilities, and those who identify as women and/or 2SLGBTQ+.

Key Dates

  • November 10th 2023  | First review date for applications
  • January 1st 2024        | Last review date for applications (no applications will be considered after this date)

Interviews will take place from November 13th through January 15th.

Funding Range

A minimum yearly stipend of $27,500 for MASc students.

Award holders (e.g., CGS-M, UBC internal fellowship) will receive a top up.

 
Reference Number

Please mention reference number GPS-57624 in all your correspondence about this Master student position.

This position will be supervised by
Interested applicants will have to gain admission to this graduate program
 
 
 
 
 

Read tips on applying, reference letters, statement of interest, reaching out to prospective supervisors, interviews and more in our Application Guide!