Have you ever analyzed some data and wondered whether there were better ways to come to the results?
And, have you ever reflected on whether such results actually match with what you expected to find?

These are just two of the questions that process mining analysts ask themselves when extracting insights from large event logs. Indeed, it can be challenging for analysts to keep track of the many steps and decisions they make, as there are many possible data analysis and cleaning steps—think of data selection or abstraction—that can be performed in many alternative ways, leading to different results. This makes it even more difficult for analysts to understand the impact of their analysis steps and choices on the results they obtain and validate that such results are aligned with their expectations and consistent with their analytical approach.

If this sounds like something you would be interested in, and you aim to help process scientists improve their analytical work, please read the job description below.

Process mining is an analytical discipline that focuses on extracting insights from event logs generated by the execution of work processes. As with other analytical disciplines, the significant involvement of human analysts to interpret raw event data and derive actionable insights remains critical. A key challenge in this area is the lack of support for analysts to reflect on and refine their analytical processes.

Our vision is to provide analysts with methods, algorithms, and tools that make them aware of how their decisions and steps relate to their analysis goals. We call this vision "Explainable Process Analytics" because it aims to enable process analysts to better understand their analysis processes and become more effective in choosing the right steps and making the right decisions for their current goal.

We're looking for a PhD candidate to join the Process Analytics on Multi-dimensional Data group under the supervision of Dr. Francesca Zerbato and Dr. Dirk Fahland and help lay the foundations for realizing this vision of "Explainable Process Analytics".

The primary focus of your PhD work will be to develop novel methods, algorithms, and tools to support process mining analysts by integrating the validation of analysis steps and results into the analysis process and into existing process mining techniques.

This focus allows for different research directions you can pick from, such as:

• Develop a framework for understanding the impact of data analysis steps. Different analytical steps, such as filtering or abstraction, can have different effects on the data and the results of the analysis, depending on how they are performed. For example, consider what happens to sequence relationships when events are filtered out one at a time. In this task, you could focus on creating algorithms, methods, or visualizations to illustrate these effects, enabling analysts to evaluate and understand the implications of their analytical choices.
• Investigate data structures and models for analytical provenance in process mining.  Analytical provenance captures the steps and choices made by analysts, allowing them to track their workflow and understand how results are generated. In this task, you could explore database models, ontologies, or other data representations to represent and query analytical provenance in process mining.
• Develop methods to validate process mining results. Process analysts need to validate their intermediate and final results based on properties—very often temporal properties about the execution of a process—that may be interesting to them or stakeholders. In this task, you could develop methods and tools to validate and compare intermediate results with ground truth data or based on user-defined properties.

These research directions give you the opportunity to learn and combine different types of research approaches, including

• Fundamental research in process mining, data models, and query languages;
• Development of algorithms, methods, and tools to support process mining analysts in their work;
• Empirical research to gather requirements from process mining analysts and validate your work with them.

Also, you will get in touch with different research fields, including process mining, databases and data analytics, and artificial intelligence. You will have the opportunity to define and shape the exact direction of your PhD work based on the listed tasks together with the research and supervision team.

Please, be aware that this is a Teaching PhD position. This means that throughout your PhD you will spend some time helping with the teaching of relevant courses (e.g., by running instruction sessions, and by correcting students' homework). You will also have the opportunity to obtain teaching qualifications.

• You have a Master's degree (or equivalent) in Computer Science, Data Science or AI.
• You are able to work in an international team and are eager to collaborate with other team members and possible industry partners involved in this research.
• You are motivated to publish your research results at leading international conferences and to travel to present your work.
• You are open to working with different research methods, ranging from foundational research to tool development and empirical research to validate your findings.
• You are fluent in written and spoken English.
• You have some knowledge of process mining from your studies or work (preferred).

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 employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,872 max. €3,670).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.