PhD Candidate in Building Energy Modeling (Remote, Research)

Job summary

This PhD position focuses on developing scalable modeling methodologies for building energy systems, integrating physical modeling with data-driven techniques. The project aims to enhance building energy modeling through the use of semantic digital twins and Large Language Models (LLMs) to optimize HVAC operations and improve energy efficiency.

Qualifications

• A two-year master's degree (120 ECTS points) or equivalent academic level.
• Solid understanding of building physics, HVAC systems, and thermodynamics.
• Experience with advanced control strategies and/or machine learning techniques.
• Familiarity with Building Information Modeling (BIM) and energy modeling software.
• Knowledge of data modeling, ontologies (RDF/OWL), or semantic web technologies is highly desirable.
• Proficiency in Python, R, or MATLAB; experience with Deep Learning frameworks (PyTorch, TensorFlow) and LLM APIs is an asset.
• Fluent English skills, both written and spoken.

Responsibilities

• Develop robust control strategies leveraging semantic data structures.
• Create workflows connecting BIM, energy simulation engines (e.g., EnergyPlus, Modelica), and control algorithms.
• Investigate LLMs as agents for querying building data and optimizing control parameters.
• Validate methodologies using real-world data from built environment monitoring.
• Publish research findings in high-impact journals and present at conferences.

Skills

• Strong analytical and critical thinking skills.
• Ability to work independently and collaboratively in interdisciplinary teams.

Tools

• Energy modeling software (e.g., EnergyPlus, Modelica).
• Programming languages: Python, R, MATLAB.
• Deep Learning frameworks: PyTorch, TensorFlow.