PhD on Leveraging Foundation Models for Sustainable Process Prediction

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.

With over 110 (assistant, associate and full) professors, almost 300 PhD and EngD students, about 1500 Bachelor students and 1000 Master students, the Department of Mathematics and Computer Science (M&CS) is the largest department of the TU/e. By performing top-level fundamental and applied research, and maintaining strong ties with industry, M&CS aims to contribute to science and innovation in and beyond the region.

Introduction

• Are you fascinated by bridging the gap between existing concepts and new application domains?
• Are you eager to work on sustainable models that can still work under data label scarcity?
• Are you excited about developing reusable models and fine-tuning existing ones?
• Are you passionate about designing multimodal models that can deal with complex process data for multiple downstream tasks?
• Are you eager to improve the way businesses analyze their processes using sustainable process mining techniques?

We are seeking a passionate and skilled PhD Fellow to contribute to our growing research team, which integrates machine learning, sustainable models and process mining to tackle challenging questions about sustainable, real-time process mining. This position will focus specifically on investigating the applicability of multimodal foundation models for sustainable process prediction.

Job Description

The Role:
We are seeking a highly motivated PhD student to join our team. In this role, you will continue our research on designing sustainable process prediction models by leveraging foundation models. Our initial research in the real-time process mining group, show promising results on designing continual learning models that mitigate catastrophic forgetting. In this role, your primary objective will be to investigate the potential of foundation models for designing multi downstream process prediction tasks that can handle multimodality. The ultimate goal is to investigate the potential of process foundation models that can help in multiple downstream tasks under a scarcity of labeled data. This will help in more sustainable prediction by reusing extensively pre-trained models.  

About the lab:
Our research group is a growing, multi-disciplinary team dedicated to leveraging machine learning and artificial intelligence for real-time process mining applications. Our core mission is to move beyond traditional static process mining solutions and to develop techniques for the management and mining of event streams with multimodalities and evolving drifts. We are part of the Process Analytics Group (PA) at Eindhoven University of Technology (TU/e), an internationally recognized research group at the forefront of process mining. You will collaborate with leading experts, contribute to ongoing research initiatives, and have access to state-of-the-art tools.

The project Context:
This position is embedded within the EuroTech PhD program, a joint PhD program that will stimulate innovative and lasting collaborations with our excellent strategic partners in EuroTech, with a focus on joint Horizon Europe proposals for external funding and on co-publications. The program will evidently boost excellence through high-impact research. Within this project, you will spend six months at a world-leading process mining lab at the Technical University of Denmark (TUD).

Job Requirements

• A (nearly completed) master’s Degree: a degree in AI, Computer Science, Data Science, Statistics, Physics, Mathematics, or a closely related field.
• Passion and Curiosity: enthusiasm for designing sustainable prediction models and reusable knowledge
• Collaboration skills: ability to work in an interdisciplinary team and interested in collaborating with industrial and academic partners.
• Programming Proficiency: strong programming skills, particularly in Python, and experience with major deep learning frameworks (e.g., PyTorch, TensorFlow/Keras).
• Machine Learning Knowledge: good understanding of fundamental machine learning concepts and deep learning architectures.
• Communication Skills: excellent written and verbal communication skills in English (C1 level). Ability to present complex ideas clearly

Desirable Skills:

• Experience with research on Foundation Models (LLMs, Transformers, etc.).
• Expertise in theoretical machine learning (generalization theory, learning dynamics, etc.).
• Experience with large-scale computing environments (HPC, cloud platforms).
• Familiarity with process mining.

Conditions of Employment

• 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 assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment. 
• 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. € 2,901 max. € 3,707).  
• 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. 

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager, Dr. Ing. Marwan Hassani, Assistant Professor, [email protected].

Visit our website for more information about the application process or the conditions of employment. You can also contact [email protected].

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 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.
• Copies of your Bachelor and Master diplomas along with your grade lists. If you are still completing your Master’s degree, please attach a list of courses with the grades you obtained so far.

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.