Position PhD-student
Irène Curie Fellowship No
Department(s) Mathematics and Computer Science
FTE 1,0
Date off 21/07/2024
Reference number V32.7552
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:
These research directions give you the opportunity to learn and combine different types of research approaches, including
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.
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:
About us
Eindhoven University of Technology is an internationally top-ranking 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.
Information
Do you recognize yourself in this profile and would you like to know more?
Please contact the hiring manager Francesca Zerbato, f.zerbato@tue.nl.
Visit our website for more information about the application process or the conditions of employment. You can also contact HRServices.MCS@tue.nl for questions related to the application process.
Are you inspired and would like to know more about working at TU/e? Please visit our career page.
Application
We invite you to submit a complete application by using the apply button. The application should include:
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.
We are an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude.
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