Build predictive maintenance models for industrial machinery using sensor data to detect faults, predict failures, and enable data-driven maintenance planning.

Scope and datasets:

• Industrial machinery sensor data
• Appliance states (ON/OFF), multi-class defect labels

Models and methods:

• ML/DL models for anomaly/fault detection and classification
• Benchmarking and accuracy evaluation
• XAI integration for interpretability

Expected deliverables:

• Deployed PdM model
• Web dashboard for monitoring and alerting
• 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)
• Solid Python and ML/DL skills; time-series modeling experience is a plus
• 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