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Digital Twin Research Intern
- Hiring from
- Nigeria
- Work type
- Hybrid
- Posted
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Join PHI Lab (Physics Hybrid Integrity Lab), an aerospace AI research practice in Kaduna, and work on real digital twin and prognostics projects, not coursework exercises.
About the internship
You'll join a small research team building physics-based digital twins and AI models for aircraft and UAV health monitoring. You'll contribute to live projects, learn how aerospace research is actually done, and finish with work you can show in your portfolio.
What you'll work on
• Physics-based simulation of aircraft and UAV components (landing gear, propulsion, structures, flight control)
• Machine learning for fault diagnosis and predictive maintenance
• Data processing, model validation, and documenting results
• Supporting research write-ups and GitHub project documentation
What you'll learn
• How digital twins connect simulation, sensor data, and maintenance decisions
• Prognostics and health management (PHM) for aircraft systems
• Python-based engineering workflows and version control
• Research practice: reproducibility, validation, and technical writing
Who should apply
• Final-year students or recent graduates in Aerospace, Mechanical, Electrical, Computer Engineering, or a related field
• Working knowledge of Python (MATLAB or Simulink is a plus)
• Interest in aviation maintenance, safety, or AI in aerospace
• Curiosity, discipline, and the ability to work independently in a hybrid setup
Nice to have (not required)
• Experience with PyTorch or scikit-learn
• Familiarity with JSBSim, FEA, or flight dynamics
• A GitHub profile or project portfolio
Details
• Location: Kaduna, hybrid
• Duration: [e.g. 3 months]
• Compensation: [stipend / unpaid with reference letter, state it clearly]
• Start date: [month/year]
How to apply
Send your CV, a link to your GitHub or portfolio, and 3 to 4 sentences on why this work interests you. Apply by [deadline].
About the internship
You'll join a small research team building physics-based digital twins and AI models for aircraft and UAV health monitoring. You'll contribute to live projects, learn how aerospace research is actually done, and finish with work you can show in your portfolio.
What you'll work on
• Physics-based simulation of aircraft and UAV components (landing gear, propulsion, structures, flight control)
• Machine learning for fault diagnosis and predictive maintenance
• Data processing, model validation, and documenting results
• Supporting research write-ups and GitHub project documentation
What you'll learn
• How digital twins connect simulation, sensor data, and maintenance decisions
• Prognostics and health management (PHM) for aircraft systems
• Python-based engineering workflows and version control
• Research practice: reproducibility, validation, and technical writing
Who should apply
• Final-year students or recent graduates in Aerospace, Mechanical, Electrical, Computer Engineering, or a related field
• Working knowledge of Python (MATLAB or Simulink is a plus)
• Interest in aviation maintenance, safety, or AI in aerospace
• Curiosity, discipline, and the ability to work independently in a hybrid setup
Nice to have (not required)
• Experience with PyTorch or scikit-learn
• Familiarity with JSBSim, FEA, or flight dynamics
• A GitHub profile or project portfolio
Details
• Location: Kaduna, hybrid
• Duration: [e.g. 3 months]
• Compensation: [stipend / unpaid with reference letter, state it clearly]
• Start date: [month/year]
How to apply
Send your CV, a link to your GitHub or portfolio, and 3 to 4 sentences on why this work interests you. Apply by [deadline].
