Are you fascinated by data science, interested in deeply analyzing all kinds of work processes using process mining, but also aware of the pitfalls and misuse potential of such methods? Work with us on shaping the next generation of responsible process mining technology that is privacy-preserving, accurate, fair, and transparent.

Job Description

Work systems often exhibit complex dynamics due to the interplay and interactions of technology, workers, and processes. Process mining can provide insights into these complex dynamics of how technology and humans interact shedding light on how people work. The key to these insights is the recording of timestamped events on each interaction that is provided by most systems. Analyzing such event data yields a detailed understanding of the process and reveals the control flow or sequence of steps of a process. It also allows to detect mismatches between the expected and actual execution of processes. Interest in process mining has drastically increased in the past years, mainly due to easy-to-use tools providing easy to understand visualization for business users. It is fair to say that process mining has a real impact on the world and business decisions.

However, process mining may lead to unfair decisions causing harm to people by amplifying the biases encoded in the data by disregarding infrequently observed or minority cases. Insights obtained may lead to inaccurate conclusions due to failing to consider the quality of the input event data. Confidential or personal information on process stakeholders may be leaked as the precise work behavior of an employee can be revealed. Process mining models are usually white box but may still be difficult to interpret correctly without expert knowledge hampering the transparency of the analysis. All such challenges are recognized under the umbrella of Responsible Process Mining (RPM).

The focus of this PhD project is to advance and develop technological solutions addressing these challenges but always in the context of the social and organizational dimension of the problems. Previous work in our group has focused on the privacy (differential privacy for event logs) and accuracy/transparency (stochastic process mining) aspects. However, depending on your background and interest the project could also focus on the very little explored dimension of fairness in process mining. Methods can be adapted from a wide range of statistical, data science, and artificial intelligence techniques and are evaluated in the context of available public real-life datasets or taken from one of the existing industry collaborations of the group.

You will work in the Process Analytics research group (https://pa.win.tue.nl/) that is conducting world process mining research and located at the birthplace of process mining as research field. Your work will contribute to theory and methods in process mining, but you will also implement solutions in our tools such as the ProM framework (https://promtools.org/) and other tools (e.g., bupaR or pm4py). You will be able to leverage our broad network of to other research groups in the whole world and local companies providing data and use cases. The position is not connected to a particular research project and funded directly by the department.

• A master's degree (or an equivalent university degree) preferably in Computer Science, Data Science, Artificial Intelligence, or a related field.
• A research-oriented attitude and strong interest in analyzing (complex) processes such as traditional business processes but also other kinds of processes in manufacturing, healthcare, customer journeys, learning, etc.
• Demonstrated experience in Process Analytics or the application of Data Science on processes.
• Strong programming skills in either Python, Java, or R. Ability build packages and libraries (beyond 'scripts' and 'notebooks'). Experience with one of the process mining frameworks is a plus.
• Ability to look beyond the narrow technological solutions and recognize the social/organizational dimension of the problem.
• Ability to work in interdisciplinary teams and interested in collaborating with industrial partners.
• Good conceptualization and communications skills.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).

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. €2,770 max. €3,539).
• 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.