Master's Thesis - Predictive Maintenance Using AI
Atlas Copco AktiebolagPublicerad: 2025-10-23
Ansök senast: 2025-12-14
Beskrivning
We are now looking for a Master's thesis student in Data Science / Machine Learning to take this research to the next level.
Location:Atlas Copco Industrial Technique, Nacka Stockholm
Project: Leveraging AI for Early Detection of Maintenance Issues in Tightening Tools
At Atlas Copco, we are continuously innovating to make industrial operations smarter and more efficient. We have developed a deep learning-based anomaly detection model capable of identifying various types of irregularities in tightening tools. This technology holds great potential to revolutionize predictive maintenance - enabling us to anticipate failures before they occur.
Your Mission
What You'll Work With
• Real-world industrial IoT and sensor data from Atlas Copco tightening tools.
• Advanced machine learning techniques, such as gradient boosting machines and deep learning (experience with deep learning is a plus).
• Data preprocessing and cleaning for model training.
• Model evaluation, including performance metrics and loss function design.
What We're Looking For
• Background in Data Science, Machine Learning, or related fields.
• Strong understanding of supervised and unsupervised learning techniques.
• Experience with Python and libraries such as scikit-learn and PyTorch.
• Curiosity, problem-solving mindset, and a drive to turn data into actionable insights.
Duration & Start
• Master's thesis project
• Start: Flexible, early 2026 preferred
Give wings to your dreams
The world is full of opportunities and we can't wait for you to explore them with us. Our caring culture and leading-edge technology enables us to innovate for a sustainable future. We believe that passionate people make it happen. With us you are empowered to explore opportunities and drive your own professional journey.
We offer a wide range of interesting job roles and many opportunities to grow. This is where it begins - Join us at the Home of Industrial Ideas.
Location:Atlas Copco Industrial Technique, Nacka Stockholm
Project: Leveraging AI for Early Detection of Maintenance Issues in Tightening Tools
At Atlas Copco, we are continuously innovating to make industrial operations smarter and more efficient. We have developed a deep learning-based anomaly detection model capable of identifying various types of irregularities in tightening tools. This technology holds great potential to revolutionize predictive maintenance - enabling us to anticipate failures before they occur.
Your Mission
- Leverage and interpret the outputs from our existing anomaly detection model.
- Develop innovative methods to aggregate model outputs and related sensor data.
- Design algorithms or metrics that can predict potential maintenance issues before a tool breaks.
What You'll Work With
• Real-world industrial IoT and sensor data from Atlas Copco tightening tools.
• Advanced machine learning techniques, such as gradient boosting machines and deep learning (experience with deep learning is a plus).
• Data preprocessing and cleaning for model training.
• Model evaluation, including performance metrics and loss function design.
What We're Looking For
• Background in Data Science, Machine Learning, or related fields.
• Strong understanding of supervised and unsupervised learning techniques.
• Experience with Python and libraries such as scikit-learn and PyTorch.
• Curiosity, problem-solving mindset, and a drive to turn data into actionable insights.
Duration & Start
• Master's thesis project
• Start: Flexible, early 2026 preferred
Give wings to your dreams
The world is full of opportunities and we can't wait for you to explore them with us. Our caring culture and leading-edge technology enables us to innovate for a sustainable future. We believe that passionate people make it happen. With us you are empowered to explore opportunities and drive your own professional journey.
We offer a wide range of interesting job roles and many opportunities to grow. This is where it begins - Join us at the Home of Industrial Ideas.
