- 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.
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).