PhD: AI for Participatory Smart Energy Buildings

Université Catholique de Louvain (UCL); Belgique; Hybrid; EUR 25,000 - 31,000; Full time

Benefits offered by this job

Fully funded PhD position

Interdisciplinary environment

Conference participation support

Job summary

Université Catholique de Louvain (UCL) invites applications for a dedicated PhD position in the Architecture and Climate group at LAB (Brussels Saint-Gilles). The project develops AI-assisted recommender systems for energy-aware building design, combining physics-informed ML surrogates with energy system planning and LLM-powered guidance.

The PhD focuses on participatory design, retrieves validated knowledge, and tests agentic tool use, with workshops and co-creation with real stakeholders,

Qualifications

• Master’s degree in energy engineering, electrical engineering, energy economics, computer science, civil/building engineering, or a related field.
• Experience with building energy, sustainable design or energy transition is advantageous.
• Programming experience (e.g., Python, Julia, or other); machine learning is a strong asset.

Responsibilities

• Review state-of-the-art ML-based surrogate models and AI recommender systems in building design.
• Design and prototype an LLM-based recommender system that combines RAG and agentic tool use (e.g. MCP) to query the simulation suite.
• Organise and run participatory workshops, and analyse results with qualitative and statistical methods.
• Publish in international journals and present at conferences.
• Contribute to teaching activities.

Skills

Python

Machine learning

Energy systems

Interdisciplinary collaboration

Education

Master’s degree in energy engineering, electrical engineering, energy economics, computer science, civil/building engineering, or related field

Tools

Python

Julia

LLM frameworks (RAG)

Job description

Université Catholique de Louvain (UCL) invites applications for a dedicated PhD position in the Architecture and Climate group at LAB (Brussels Saint-Gilles). The project develops AI-assisted recommender systems for energy-aware building design, combining physics-informed ML surrogates with energy system planning and LLM-powered guidance.

The PhD focuses on participatory design, retrieves validated knowledge, and tests agentic tool use, with workshops and co-creation with real stakeholders,