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PostDoc on Process Mining for Digital Twins using Knowledge Graphs

PostDoc on Process Mining for Digital Twins using Knowledge Graphs

Complex systems generate very large networks of event data. We offer a PostDoc in the AutoTwin EU project to develop algorithms and technology to create Digital Twins of industrial processes and value chains from networks of event data by leveraging knowledge graphs.
(Postdoctoraal) onderzoeker
Irène Curie Fellowship
Mathematics and Computer Science


Research project description

Digital twins of physical systems are a key enabler for reducing costs and improving the efficiency of processes and reducing waste in value chains. The EU-funded AutoTwin project is developing breakthrough methods for automatically generating and maintaining Digital Twins from system data. The research challenge lies in using event data of entire systems and value chains to generate accurate Digital Twin models describing the complex interactions of multiple products, machines, and actors – up to identifying cause-effect relations and enabling what-if analyses.

Within AutoTwin, the Process Analytics research group of TU Eindhoven has the ambition to realize a foundational knowledge graph-driven approach for process mining that supports the full cycle of Digital Twin management: event data integration, simulation model learning, model validation and what-if scenario generation, cause-effect analysis, and conformance checking.

The PostDoc position offers the unique opportunity to develop next-generation process mining technology that can analyze and simulate entire systems, to shape new theories, realize them in technology that are transferred into an industrial context.

Job description

You will be joining the Process Analytics group of TU Eindhoven in their mission of laying the foundations of an entirely new research field of process mining for entire systems and digital twins.
Your main scientific focus will be in researching, developing, and implementing novel models and efficient algorithms for process mining over (very large) knowledge graphs; and also publish your results as scientific articles and open-source software.

You actively collaborate with multiple European universities and research institutes in developing techniques for learning, using, and maintaining simulation-based digital twins, and their validation in industrial use cases in the AutoTwin project. Part of your time, you dedicate to standardizing the developed techniques and their dissemination through developing tutorials, training, and communication material. You actively support the Process Analytics group in coordinating a work package of the AutoTwin project.

You will have the opportunity to supervise Bachelor and Master projects and to participate in teaching activities in your area of expertise.


  • Motivated researcher with a PhD related to process mining, digital twins, knowledge graphs and/or graph algorithms.
  • The ideal candidate has research experience in one of the above areas, the eagerness to quickly acquire required knowledge in other areas, and affinity to working with complex data.
  • Ability to conduct high quality academic research, reflected in demonstratable output and implementation skills for translating ideas into research prototypes.
  • A sociable person who enjoys working in a dynamic, interdisciplinary team and solving problems together.
  • The ability to untangle complex requirements and interests into a structured approach that is completed on schedule.
  • Excellent (written and verbal) proficiency in English, good communication skills.
  • Independent and responsible.
  • Can-do mentality.


A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

  • Full-time appointment for 1 year with the possibility of an extension.
  • Salary in accordance with the Collective Labour Agreement for Dutch Universities,
  • scale 10.
  • A year-end bonus of 8.3% and annual vacation pay of 8%.
  • High-quality training programs on general skills, didactics and topics related to research and valorization.
  • An excellent technical infrastructure, on-campus children's day care and sports facilities.
  • Partially paid parental leave and an allowance for commuting, working from home and internet costs.
  • A TU/e Postdoc Association that helps you to build a stronger and broader academic and personal network, and offers tailored support, training and workshops.
  • A Staff Immigration Team is available for international candidates, as are a tax compensation scheme (the 30% facility) and a compensation for moving expenses.

Informatie en sollicitatie

About us

Eindhoven University of Technology is an internationally top-ranked university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.


Do you recognize yourself in this profile and would you like to know more?
Please contact the hiring manager, dr. Dirk Fahland, d.fahland[at]

Visit our website for more information about the application process or the conditions of employment. You can also contact HR Services M&CS, HRServices.MCS[at]

Are you inspired and would like to know more about working at TU/e? Please visit our career page.


We invite you to submit a complete application using the apply-button. The application should include a:

  • Cover letter in which you describe your motivation and qualifications for the position.
  • Curriculum vitae, including a list of your publications and the contact information of three references.
  • List of five self-selected ‘best publications’.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.