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PhD Scholarship in the Field of Improving Overall Equipment Efficiency in Silicon Crystal Growth Production with Digital Manufacturing Technologies

Technical University of Denmark - Department of Mechanical Engineering

Qualification Type: PhD
Location: Lyngby - Denmark
Funding for: UK Students, EU Students
Funding amount: Allowance will be agreed upon
Hours: Full Time
Placed On: 25th March 2019
Closes: 28th April 2019

The Section for Manufacturing Engineering at the Department of Mechanical Engineering, Technical University of Denmark has an open position in the field of Improving overall Equipment Efficiency in Silicon Crystal Growth Production with Digital Manufacturing Technologies. Topsil GlobalWafers has a full-scale production facility and all work will address real production challenges.

The Section for Manufacturing Engineering at the Department of Mechanical Engineering performs research based on a multi-disciplinary use of process technology, materials science, solid and fluid mechanics, and thermodynamics in the analysis, modelling and development of manufacturing processes. The research objective of the Section is to promote ‘Precision Manufacturing’ to meet the performance, durability, reliability, size, and cost requirements of modern products.

The PhD project is carried out in the framework of the Horizon2020 Marie-Curie Innovative Training Network DIGIMAN4.0 “DIGItal MANufacturing Technologies for Zero-defect Industry 4.0 Production”. Please look at the project website ( before reading further. The PhD project is related to the Early Stage Researcher (ESR) position no. 3 of the DIGIMAN4.0 project.

Responsibilities and tasks

The position focuses on research in the area of precision manufacturing engineering and manufacturing digital technologies, with focus on IoT, Big Data analytics, system integration, simulation and autonomous robots. It is the goal for the current PhD project to develop in-depth root-cause analysis and detailed Pareto analysis of failure mode using big data approach to achieve intelligent predictive maintenance.

The project will require the integration and development of necessary manufacturing and digital technologies enabling advancements in the process capability and optimization of pure silicon wafers industrial production.

The PhD project will have the following objectives:

  • Intelligent use of big data to in-depth analysis of machine failure mode and increase availability of critical equipment
  • Develop a model for accurate control of the product specifications and to optimize production costs.
  • Implement a vision system data fusion for an improved process control/measurement and reduced number of failures due to human errors.

Candidates should 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. 

  • M.Sc. in Mechanical Engineering, Computer Science or similar
  • Knowledge about statistical quality control methods (e.g. statistical process control, design of experiment, big data analytics)
  • Knowledge and experience with programming
  • Knowledge and experience with algorithms for Massive Data Set
  • Knowledge and experience with digital manufacturing technologies such as e.g. computer vision system, simulation
  • Knowledge and experience with measuring technologies and quality control methods
  • Ability to work independently, to plan and carry out complicated tasks, and to be a part of a large, dynamic group
  • Good communication skills in English, both written and spoken


To apply, please read the full job advertisement at

Application deadline: 28 April 2019 

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* Salary has been converted at the prevailing rate on the date placed
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