Thales is a global technology leader trusted by governments, institutions, and enterprises to tackle their most demanding challenges. From quantum applications and artificial intelligence to cybersecurity and 6G innovation, our solutions empower critical decisions rooted in human intelligence. Operating at the forefront of aerospace and space, cybersecurity and digital identity, we're driven by a mission to build a future we can all trust.

In Singapore, Thales has been a trusted partner since 1973, originally focused on aerospace activities in the Asia-Pacific region. With 2,000 employees across three local sites, we deliver cutting-edge solutions across aerospace (including air traffic management), defence and security, and digital identity and cybersecurity sectors. Together, we're shaping the future by enabling customers to make pivotal decisions that safeguard communities and power progress.

TOPIC : Condition-based Predictive Maintenance for Critical Systems

Description

Thales is a major supplier of critical systems, e.g. defense sensors and avionics parts. One of our missions is to revolutionize the way critical systems are maintained, ensuring maximum uptime, efficiency, and reliability. Joining the team means to be at the forefront of developing and implementing advanced machine learning models to predict and prevent equipment failures. Your work will directly impact the reliability and efficiency of our critical systems, ensuring they operate at peak performance. You will collaborate with a multidisciplinary team of engineers, data scientists, and domain experts to design, develop, and deploy predictive maintenance solutions.

Responsibilities:

Collect / clean / transform / annotate data
Survey / select / develop / compare candidate AI models
Develop software codes to automate entire machine-learning lifecycle that meet corporate software engineering guidelines

Requirements:

Hands-on experience with machine learning
Proficient in at least two programming languages e.g. shell, Python, R, Matlab, C
Preferably experience with various supervised / unsupervised AI models
Preferably experience in making, serving and proxying API calls e.g. HTTP
Keen interests in machine-learning operations for scale up and fast iteration
Team player

Expected Outcomes

Demonstration of a working system with well-defined end-to-end test scenarios
Documentation of the finding, design and test cases / results
Team sharing and communication of various forms, including but not limited to live demo, video and workshops

Commitment:

Able to commit on a full-time basis for ideally 6 months (or a minimum of 5 months) from Jan 2026.

At Thales, we're committed to fostering a workplace where respect, trust, collaboration, and passion drive everything we do. Here, you'll feel empowered to bring your best self, thrive in a supportive culture, and love the work you do. Join us, and be part of a team reimagining technology to create solutions that truly make a difference - for a safer, greener, and more inclusive world.

QML Research Intern