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.

• Dominic Liao-McPherson | dliaomcp {at} mech.ubc.ca | https://mech.ubc.ca/dominic-liao-mcpherson/
• Zhengbo Zou                    | zhengbo {at} civil.ubc.ca    | https://civil.ubc.ca/zhengbo-zou/

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.

This posting is for the UBC Vancouver campus in British Columbia, Canada.

Please refer to reference number EU-57624 during correspondence about this position.

To express interest and initiate contact to the principal investigator applicants are encouraged to follow the instructions as provided in the position description or to submit an enquiry form on the position posting. Please note that applicants will have to complete an online application to the University of British Columbia and gain admission as a graduate student to the university. Applicants should review the graduate program profile to learn about admission requirements, application deadlines, program duration, tuition, or to read student and alumni stories. Please visit the researcher profiles of the supervisors (0, 2) for this position to learn more about their research.

About UBC
The University of British Columbia is a global centre for research and teaching, consistently ranked among the top 20 public universities in the world. Since 1915, UBC's entrepreneurial spirit has embraced innovation and challenged the status quo. UBC encourages its students, staff and faculty to challenge convention, lead discovery and explore new ways of learning. At UBC, bold thinking is given a place to develop into ideas that can change the world.

Research Master's at UBC Vancouver
UBC is a public university with a comprehensive program offering - covering all major academic disciplines and degree levels, with both research- and course-based program options. More than 50,000 students attend UBC Vancouver. As a research master’s student at UBC Vancouver, you'll be part of a diverse community of over 10,000 graduate students at an institution which is globally connected and attracts the highest-calibre research faculty. Work side by side with passionate scholars in modern facilities and with the support of numerous professional development workshops and services.

You are encouraged to review the awards and scholarship database to determine funding opportunities for your master's degree. UBC invests significantly into its research programs and, combined with the International Tuition Award, offers most research-based Master programs at a comparable low tuition rate for domestic and international students alike.

About Vancouver
Vancouver is a dynamic, cosmopolitan and progressive city, consistently ranked as one of the top cities to live in the world. Canada's third largest city has it all: sea, parks, mountains, beaches, and four seasons per year, including beautiful summers and mild, wet winters with snow in the mountains. It's the perfect backdrop to your academic research.