Master Scholarship on Deep Learning for Real-time Print Artifact Detection

Eindhoven University of Technology, Netherlands is inviting excellent students to apply for a master position. The funding allows successful candidates to work for two years in the first instance. There is no application deadline for this position.

An important part of the PDEng programme is the design project which will be performed at Oc technologies. The project is spread over two years and aims to develop your design engineers’ abilities. In the project, a specific design objective must be reached within a restricted period of time and with limited means. In accordance with the supervising professor the candidate will be given the opportunity to gather specialized knowledge for the design project already in the first year of your Traineeship. You will be coached by experienced design engineers from industry and/or by TU/e staff with clear and relevant design experience. You will acquire independence and learn to make choices and work well in a project-based manner. In this PDEng project, you need to develop novel techniques to ensure that future printers produce consistent high quality prints. In most print-sites the quality is currently still checked manually by operators which costs a lot of time while being prone to errors. Checking frequency is low and many print quality related problems may go unnoticed.

The focus of this project will be on inkjet printers and the artifacts that are likely to occur by printing. We would like to detect the print artifacts with a camera in real-time within the printer. We want to detect the artifacts before the print leaves the printer (preferably as soon as possible) and communicate this to the operator and to a decision making unit in the printer.

The aim is to detect if an artifact has occurred by applying deep learning. The learning system will primarily process the images taken by the in-line vision system but can be complemented by the information obtained from other types of sensors as well. The detection has to be fast enough to allow the system to discard the paper containing the artifact into an error location and restore the required print sequence without generating too much waste.

Electronic Sytems Group

Eindhoven University of Technology (TU/e) is a world-leading research university specializing in engineering science & technology. The Department of Electrical Engineering is responsible for research and education in Electrical Engineering. The discipline covers technologies and electrical phenomena involved in computer engineering, information processing, energy transfer and telecommunication. The department strives for societal relevance through an emphasis on the fields of smart sustainable systems, the connected world and care & cure. The TU/e is the world's best-performing research university in terms of research cooperation with industry (#1 since 2009). The Electronic Systems group consists of seven full professors, ten assistant professors, several postdocs, about 50 PDEng and PhD candidates and support staff. The ES group is world-renowned for its design automation and embedded systems research. It is our ambition to provide a scientific basis for design trajectories of electronic systems, ranging from digital circuits to cyber-physical systems. The trajectories are constructive and lead to high quality, cost-effective systems with predictable properties (functionality, timing, reliability, power dissipation, and cost). Design trajectories for applications that have strict real-time requirements and stringent power constraints are an explicit focus point of the group.

We offer a fixed-term, 2 year position in a research group with an excellent reputation. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, including:

Candidates will be selected based on graduation mark and proficiency at university including consideration of the reputation of the university, relevant experience and skills, writing skills and publications, work experience as well as performance in relevant modeling exercises and interviews.


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