Explanatory Data Analysis group

Vacancy: PhD candidates Machine Learning for Digital Twins

The Faculty of Science, Leiden Institute of Advanced Computer Science is looking for

two PhD candidates Machine Learning for Digital Twins (1.0 fte)

Vacancy number 20-244

Project description

The development of reliable and agile digital twins of high-tech systems and materials is key to enabling shorter time-to-market, zero-defect and flexible manufacturing systems with accurate predictive maintenance. This crucial development is currently hampered by the lack of synergy between model-based engineering and data-driven/artificial intelligence approaches. The DIGITAL TWIN programme will develop key-enabling technologies for full digitization of the value chain of high-tech systems and materials by the integration of data-driven learning approaches and model-based engineering methods.

PhD candidate 1: Automated hybrid model selection using the minimum description length principle. The researcher will develop statistical methods for the automated selection of hybrid models that appropriately trade-off complexity with accuracy, resulting in optimal generalizability. Theory and methods will be based on the minimum description length (MDL) principle. Starting from first-principle models, data and machine learning will be used to learn how the parameters of these models vary with other factors. The resulting methods will be applied to the HIsarna advanced steel making process, at the HIsarna pilot plant at Tata Steel IJmuiden.

PhD candidate 2: Feature and data subset selection for contextual anomaly detection using hybrid models. The researcher will develop methods for feature selection and data subset selection that leverage the advantages of hybrid models, enabling more accurate contextual anomaly detection while making it unnecessary to collect and store all data. The selected features and data will be combined with existing machine learning methods to obtain accurate preventive maintenance, ensuring low maintenance costs and high system availability. The resulting methods will be applied to health monitoring and preventive maintenance for high-end printers (with Océ) and wafer handler systems (with VDL and ASML).

Both candidates will be embedded in the Explanatory Data Analysis group at the Leiden Institute of Advanced Computer Science. More information about the group can be found at the website.

The NWO AES Perspectief programme DIGITAL TWIN is a comprehensive, five-year research programme on the development of digital twin and digital twinning methods, financed by the domain of Applied and Engineering Sciences (AES) of the Dutch Research Council (NWO). This collaborative programme involves six universities, i.e., University of Groningen, Eindhoven University of Technology, TU Delft, University of Twente, Leiden University, and Tilburg University, and twelve industrial partners.

Key responsibilities

  • Conduct original and novel research in the field of machine learning for digital twins;
  • Apply methods to use cases, in collaboration with industrial partners;
  • Actively participate and collaborate in national DIGITAL TWIN consortium;
  • Publish and present scientific articles at international journals and conferences;
  • Contribute to educational activities;
  • Write a dissertation.

Selection criteria

  • A MSc degree in Computer Science, Statistics, Data Science, Artificial Intelligence, or a related field;
  • Good knowledge of and experienced with machine learning, data mining, and statistics;
  • Knowledge of and/or interest in high-tech system engineering (in particular model-based engineering);
  • Highly motivated to both perform foundational machine learning research and apply the developed methods in an industrial environment;
  • Creative, ‘making things work’ mentality, independent, and communicative team player;
  • Experienced with writing scientific manuscripts and good academic writing skills;
  • Excellent programming skills (preferably C++ and/or Python);
  • Interested in participating in educational activities;
  • Excellent proficiency in English (oral and written).

Research at our faculty

The Faculty of Science is a world-class faculty where staff and students work together in a dynamic international environment. Our people are driven by curiosity to expand fundamental knowledge and to look beyond the borders of their own discipline. The research carried out at the Faculty of Science is diverse, ranging from mathematics, artificial intelligence, computer science, astronomy, physics, chemistry and bio-pharmaceutical sciences to biology and environmental sciences. The faculty has grown strongly in recent years and now has more than 1,300 staff and almost 4,000 students. We are located at the heart of Leiden’s Bio Science Park, one of Europe’s biggest science parks, where university and business life come together. For more information, see the website.

The Leiden Institute of Advanced Computer Science (LIACS) is the Artificial Intelligence and Computer Science Institute in the Faculty of Science of Leiden University. We offer courses at the Bachelor and Master of Science level in Artificial Intelligence, Computer Science, ICT in Business, Media Technology, and Bioinformatics. According to an independent research visitation, we are one of the foremost computer science departments of the Netherlands. We strive for excellence in a caring institute, where excellence, fun, and diversity go hand in hand. We offer a clear and inviting career path to young and talented scientists with the ambition to grow. For more information about LIACS, see the website

Terms and conditions

We offer a full-time 1 year term position for initially one year. After a positive evaluation of the progress of the thesis, personal capabilities and compatibility the appointment will be extended by a further three years. Salary range from € 2.325,- to €2.972,- gross per month (pay scale P in accordance with the Collective Labour Agreement for Dutch Universities).

Leiden University offers an attractive benefits package with additional holiday (8%) and end-of-year bonuses (8.3%), training and career development and sabbatical leave. Our individual choices model gives you some freedom to assemble your own set of terms and conditions. Candidates from outside the Netherlands may be eligible for a substantial tax break.

All our PhD students are embedded in the Leiden University Graduate School of Science. Our graduate school offers several PhD training courses at three levels: professional courses, skills training and personal effectiveness. In addition, advanced courses to deepen scientific knowledge are offered by the research school.


Leiden University is strongly committed to diversity within its community and especially welcomes applications from members of underrepresented groups. We wish to reflect society both in age, gender and culture, as we believe that this would optimize the dynamics in our organization. Therefore, we support and understand the need for a work/life/family balance and consequent varying working hours and places. In the Netherlands, a maternity allowance is standard for 16 weeks. Child care is available at and near the Bio Science Park.


Enquiries can be made to Matthijs van Leeuwen, e-mail: m.van.leeuwen@liacs.leidenuniv.nl.


To apply for this vacancy, please send an email to Matthijs van Leeuwen: m.van.leeuwen@liacs.leidenuniv.nl. When applying, please use the following subject: “DIGITAL TWIN – Your name”. Please ensure that you attach the following additional documents, quoting the vacancy number:

  • Curriculum vitae (CV);
  • Motivation letter (1 page maximum);
  • Grade list and MSc degree (or expected graduation date);
  • (Draft of) MSc thesis;
  • Two reference letters (at least one from your Master thesis supervisor);
  • Link to public code repository (e.g., GitHub) or example of written code base.

Only applications received no later than July 10, 2020 can be considered. Applicants may be contacted with requests for additional information and/or exploratory conversations before the closing date.

Original version at universiteitleiden.nl