Develop and evaluate NILM (Non-Intrusive Load Monitoring) approaches to disaggregate building-level energy into appliance-level usage for industrial settings.

Scope and datasets:

• Industrial building energy data
• Multiple sampling frequencies (seconds, minutes, hourly)

Models and methods:

• Machine Learning and Deep Learning approaches for NILM
• Benchmarking across models and metrics
• Appliance-level disaggregation and ON/OFF state detection

Expected deliverables:

• Industrial-grade NILM model
• Visualization interface/dashboard for appliance-level insights
• Research paper-quality write-up

Technical environment:

• Python, PyTorch, TensorFlow, scikit-learn
• Streamlit, Dash, Plotly, Gradio
• Git, Jupyter; cloud deployment optional

Profile:

• Final-year engineering student (AI, Data Science, CS, Energy, Automation)
• Strong Python and ML/DL foundations; familiarity with time-series/signal processing
• Dashboard development experience is a plus
• Good communication, teamwork, curiosity, autonomy

What we offer:

• Real-world Industry 4.0 and energy-tech projects
• Mentorship from an experienced AI/Data Science team
• Opportunity to co-author a scientific publication
• Immersion in a fast-growing scale-up; potential for future collaboration

How to apply:

• Email your CV/Resume and (optional) GitHub/Portfolio
• Important: mention the project title in the email subject

📧 Pour postuler: feres.jerbi@wattnow.io