[AISWorld] PhD Position in Explainable Process Analytics - Eindhoven University of Technology

[Zerbato, Francesca]

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PhD in Explainable Process Analytics (Eindhoven University of Technology)
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Job Description
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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.

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Job Requirements
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- Master's degree (or equivalent) in Computer Science, Data Science, or AI.
- Ability to work in an international team and collaborate with industry partners.
- Motivation to publish research results at international conferences.
- Openness to working with diverse research methods.
- Fluency in written and spoken English.
- Knowledge of process mining (preferred).

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Conditions of Employment
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- Full-time employment for 5 years, with a 20% teaching load.
- Salary and benefits in accordance with the Collective Labour Agreement for Dutch Universities.
- Year-end bonus and annual vacation pay.
- High-quality training programs and support for personal development.
- Excellent technical infrastructure, on-campus childcare, and sports facilities.
- Commuting, working from home, and internet allowances.
- Support for international candidates, including a tax compensation scheme.

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How to Apply
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For more information about the requirements for the position and to apply, please check the vacancy at:

https://jobs.tue.nl/en/vacancy/%E2%80%8Ephd-in-explainable-process-analytics-1089837.html

Please contact the hiring manager, Francesca Zerbato: mailto:f.zerbato at tue.nl
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