JOB DESCRIPTION

Are you fascinated by developing algorithms that uncover hidden insights in complex real-world process data?
Are you eager to improve the way businesses analyze their processes using process mining techniques?
Are you excited about sharing your knowledge with Bachelor students and gaining valuable teaching experience in Data Analytics?

JOB DESCRIPTION

Conformance checking helps us understand how well the process behavior recorded in event logs matches the process behavior prescribed by a process model. The goal of this project is to advance conformance checking methods by integrating timed and stochastic aspects of process behavior and optimizing alignments across entire event logs. As a PhD student on this project, you will:

• Analyze Effects of Timed and Probabilistic Features of a Process on the Quality of Alignments: Review and validate existing algorithms that generate trace-specific alignments and explore their limitations in handling time-sensitive and stochastic behaviors.
• Develop Enhanced Global Alignment Algorithms: Design and implement new algorithms that optimize the alignment for the entire event log, considering all traces, while also taking into account time-dependent variations and stochastic behavior.
• Create Realistic Test Data for Evaluation: Develop methods for generating synthetic event logs that reflect typical behavioral deviations, timing variations, and stochastic elements to thoroughly test the effectiveness of the global alignment algorithms.
• Contribute to Process Mining Tools: Enhance existing process mining tools by integrating these improved conformance checking techniques. These tools will enable companies and researchers to assess process models more effectively under realistic, time-sensitive, and uncertain conditions, considering the event log as a whole.

As part of your PhD, you will have the opportunity to teach Data Analytics to Bachelor students, helping them develop skills essential for their future careers. You will engage with motivated students, design hands-on learning experiences, and guide them in applying data analytics techniques to real-world challenges. Teaching will not only enhance your communication and mentoring skills but also deepen your understanding of key concepts, making you a stronger professional.
As a PhD candidate, you will be part of the Process Analytics Group IPA) at Eindhoven University of Technology (TU/e). The PA group is an internationally recognized research group at the forefront of process mining and conformance checking. You will collaborate with leading experts, contribute to ongoing research initiatives, and have access to state-of-the-art tools.
Your research will enhance the reliability of process mining tools used by businesses, healthcare institutions, and government organizations to optimize processes and improve decision-making. By making conformance checking more effective, you will help organizations detect inefficiencies, reduce errors, and enhance compliance in real-world operations.

JOB REQUIREMENTS

• A master’s degree (or an equivalent university degree) in Computer Science, Data Science, Statistics or a related field.
• A research-oriented attitude, experience in data analysis and problem-solving.
• Proficiency in algorithms and programming, including Python.
• Ability to work in an interdisciplinary team and interested in collaborating with industrial partners.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 or C2 level).

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.

Please refer the Job description for details