Stage 2- Title: Development of an Operational 3D Digital Twin Based on Building Usages

This internship focuses on the design and development of an operational 3D digital twin of a tertiary building floor, connected to real usage and energy consumption data. The objective is to create a living digital twin, faithful to the spatial structure of the building and capable of evolving in real time according to actual usages.

The digital twin will represent zones, offices, and electrical equipment (e.g. washing machines, refrigerators, heaters, TVs, cafeteria equipment) and will be navigable in 3D. It will be connected to real-time data streams (LoRaWAN, MQTT) and enriched with machine learning models operating in parallel to enable prediction, simulation of usage scenarios, and decision support.

The project relies exclusively on open-source technologies and aims to deliver a reusable platform for smart building monitoring and analysis.

Working methodology and Responsibilities:

• Design and develop a 1:1 scale 3D digital twin of a building floor based on 2D plan
• Structure the digital twin hierarchically according to zones: floors, offices, equipment
• Implement real-time data integration using MQTT and LoRaWAN pipelines
• Integrate the core machine learning models (ALREADY DEVELOPPED) operating in parallel with the 3D model for consumption prediction and anomaly detection
• Develop usage-based simulation scenarios (like equipment scheduling or changes, energy-saving modes).
• Validate the system through realistic operational scenarios.

Do you have experience in REST?, * EU nationality or French work/student permit

• Background in computer science, data science, or smart systems
• Experience or interest in 3D environments, simulation, or game ureal engines
• Python programming (data processing, APIs, ML basics)
• Knowledge of IoT, MQTT, or time-series data is a plus
• Familiarity with BIM or digital twins is appreciated

Deliverables:

• Digital twin data model that includes navigable 3D of a building (walls, floor and equipments)
• Machine learning models for consumption prediction and anomaly detection
• Dashboards and KPIs (Grafana or equivalent)
• Manual of use and research reports,

What You Will Learn

• Design and implementation of operational digital twins
• Integration of real-time IoT data into 3D environments
• Usage-based simulation and scenario analysis
• Application of machine learning to smart buildings
• Open-source architectures for smart infrastructure
• Research and industrial prototyping methodologies, * CAD/3D model familiarity (OBJ, FBX, Blender)
• Python
• Understanding of MQTT or REST APIs for real-time data