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.
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.
Do you recognize yourself in this profile and would you like to know more?
Please contact the hiring manager Felix Mannhardt, Assistant Professor, f.mannhardt[at]tue.nl.
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 by using the APPLY NOW button on the vacancy page. 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.
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.