Professor Jenn McArthur at Toronto Metropolitan University is recruiting PhD candidates for her research group in Building Science, with a focus on building energy modeling, HVAC systems, energy management, and the application of machine learning and deep learning techniques. Two PhD positions are available: one for candidates with expertise in building energy modeling, thermodynamics, and heat transfer (especially in HVAC), and another for candidates with advanced deep learning skills to lead research on integrating learning into HVAC equipment emulators. The research involves developing retrofit decision-making tools and building emulator development using tuned archetypes and surrogate models. Applicants should have a strong background in relevant engineering fields, such as civil or mechanical engineering, and experience or willingness to learn machine learning. For the deep learning-focused position, a background in computer science, data science, or machine learning, combined with knowledge of building HVAC systems, is required. All studies are in-person in Toronto, and domestic applicants are strongly preferred, though exceptional international candidates will be considered. The group is committed to diversity and encourages applications from underrepresented groups, including BIPOC, women, LGBTQ+, and disabled students. First-generation PhD students or those needing accommodations are encouraged to reach out for support. All PhD students in the group receive a stipend of $35,000/year plus scholarships (from Fall 2026) to subsidize tuition. The application process requires submission of a cover letter, CV, unofficial transcripts from Bachelors and Masters degrees, a sample of academic writing, and contact details for two references, all combined into a single PDF and sent via email. The successful candidates will also need to complete the formal application process at Toronto Metropolitan University, with final acceptance determined by the admissions committee. Applications will be considered until the positions are filled, with a start date in Spring (May 2026) or Fall (September 2026). Key research areas include building science, HVAC, energy management, machine learning, deep learning, and emulator development. This is an excellent opportunity for students interested in the intersection of engineering, data science, and sustainable building technologies.