PhD Scholarship in Platform-based Manufacturing System

If you are ready to launch your research career in advanced manufacturing and want to build cutting-edge skills in AI and real-time data-driven production, this PhD opportunity is your gateway. As part of MADE REACT (Resilient manufacturing systems through AI-powered and digitally Connected Technologies)—a national collaboration involving 23 leading companies, five Danish universities, and three research and technology organizations—you will be at the forefront of shaping the future.

This PhD project offers you the opportunity to develop cutting-edge competencies in digital manufacturing platforms, data engineering, and AI-powered maintenance solutions—key capabilities for driving efficiency in the competitive semiconductor industry and beyond. You will explore how operational platforms can unify production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures.

Your research will focus on addressing current bottlenecks in data and system management, especially around data quality, metadata governance, and the integration of machine data for long-term monitoring. Through a hybrid approach combining physical models and machine learning, you will help develop new methods for understanding complex failure mechanisms—an area where existing industrial knowledge remains limited.

The project will be executed in three systematic phases: first, establishing a robust data foundation; second, designing a modular and open digital platform; and third, developing a predictive maintenance application as a proof of concept.

Responsibilities and qualifications

This PhD project aims to develop scalable digital solutions that boost operational efficiency in the Silicon manufacturing sector. It will explore how integrating platform-based manufacturing concepts improves decision-making, reduces costs, and enhances cross-functional collaboration. The project also addresses practical implementation challenges in existing industrial settings. A key focus is demonstrating how predictive analytics can improve maintenance and production planning. By comparing data-driven and traditional methods, the project will highlight the tangible benefits of digital platforms. Ultimately, it aims to deliver a flexible, open solution adaptable across industries.

In addition to your core research responsibilities, you will have the opportunity to share your findings through scientific publications and presentations at international conferences.

We are looking for a candidate who: 

• Think creatively and approaches problems with an open, computational mindset.
• Is motivated to bring data-driven insight to physical systems.
• Is enthusiastic about refining cutting-edge research ideas and validating them through practical implementation. 

In addition, candidates must have prior experience in at least three of the following areas: 

• Python programming 
• Develop LLM-based tools to automate data connector generation for data ingestion.
• Design and implement a multi-layered storage strategy for scalable PBM
• Build a microservice-based digital platform enabling plug-and-play analytics apps.
• Create user interfaces to simplify deployment of ML models on the platform.
• Develop hybrid predictive maintenance models combining physical and ML approaches.
• Test, validate, and integrate developed solutions in real industrial environments. 

You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree.

Application procedure 

To apply, please read the full job advertisement by clicking the 'Apply' button

Read more about DTU at www.dtu.dk and about DTU Construct at www.construct.dtu.dk

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