Background
Quintus Technologies Aftermarket business is expanding rapidly, focusing on spare parts, services, upgrades, and service agreements. The exceptional quality of our pressure technology ensures equipment life cycles that often exceed 50 years.
Throughout these extended operational periods, electrical subsystem overhauls, upgrades, and retrofits are common. Managing the life cycle of long-lasting equipment presents both challenges and opportunities — particularly in assessing component status, predicting maintenance needs, and optimizing upgrades.
To enhance customer satisfaction, ensure cost efficiency, and extend equipment life time, Quintus aims to use AI technology to build a data-driven Life Cycle Management (LCM) System.
Task description
To develop an AI-based life cycle management system that enables real-time monitoring, assessment, and optimization of equipment condition.
- Assessment of Life Cycle State of old Bill of Materials (BOM)
- Collecting life cycle data from supply chain
- Comparing old machines with latest baseline assessing best way for upgrade
Suitable background
Master of Science with specialization in data science, AI or equivalent.
Application Information
The thesis will take place at Quintus office in Västerås.
The selection will be ongoing so therefore apply as soon as possible. Send your CV together with grades to recruitment@quintusteam.com