Are you looking to shape the future of artificial intelligence (AI) and to develop yourself in the growing area of human-centric AI? Join us in this fully funded PhD project! In the next four years, our project will focus on developing cutting-edge techniques that enhance the transparency and performance of predictive models in scenarios involving sequential decision-making.

Are you looking to shape the future of artificial intelligence (AI) and to develop yourself in the growing area of human-centric AI? Join us in this fully funded PhD project! In the next four years, our project will focus on developing cutting-edge techniques that enhance the transparency and performance of predictive models in scenarios involving sequential decision-making. One example of such sequential decisions comes from the clinical context, where clinicians must make a series of decisions about treatment options as the condition of a cancer patient progresses. While explainable AI (XAI) and algorithm transparency has been one of the most popular areas in AI for some years, most of the works have focused on the relation to non-sequential predictions (e.g., image classification) while sequential decision-making problem is unexplored. In this project, you will address this research gap by aiming for two objectives:

1. Develop advanced prediction models that excel in scenarios of sequential decision-making by striking a delicate balance between predictive performance and transparency. Model performance will be evaluated with existing and potentially new data in an application domain of sequential decision-making (e.g., cancer treatment decisions).

2. Propose an innovative methodology for quantifying, measuring, and validating model explanations in collaboration with domain experts, ensuring the transparency and trustworthiness of AI-driven decisions. Efforts will also be made to understand the decision processes of the domain experts (e.g., clinicians) and to properly integrate the models and their interactions with them into their working context. 

The project will follow a human-centered perspective and takes an interdisciplinary approach that combines machine learning, behavioral sciences, and human-centered design. Therefore, you will be supervised by a team of experts from both the Information System group (Dr. Isel Grau, Dr Pieter Van Gorp) and the Human-Technology Interaction group (Dr. Chao Zhang) at the Department of Industrial Engineering and Innovation Sciences. The project is also embedded in the EU-funded project 'ENFIELD: European Lighthouse to Manifest Trustworthy and Green AI', where TU/e is leading to push the boundaries of human-centric AI. Therefore, you will have the opportunity to collaborate with an international team of experts and broaden your network within the project consortium.  

As a PhD candidate in this project, you are expected to conduct top-tier scientific research, publish in peer-reviewed journals, and present at international conferences. You will also actively manage and coordinate the Human Centric AI pillar in the ENFIELD project, demonstrating project management skills and collaborating with international experts. Furthermore, you will have access to TU/e's PhD-level courses and relevant summer schools. This comprehensive experience covering the human and technical side of AI will make you a sought-after researcher for both universities and AI companies upon completing your defense.