PhD Candidate Human-Centered Interpretable Machine Learning

**Job Description**
This project focuses on developing human-centered interpretable machine learning theories and algorithms to overcome the limitations of current explainable AI methods and integrate domain expertise while maintaining accuracy. The research will explore pattern-based modeling, hybrid models (e.g., combining pattern-based models with neural networks), and methods enabling interpretable models to learn from deep representations. A key objective is to develop theory and algorithms for hybrid model selection that leverages domain knowledge through interactive learning, building upon the minimum description length (MDL) principle. The resulting methods will be evaluated on real-world case studies, particularly within the health care domain, to demonstrate their potential for discovering novel insights from data. The candidate will be embedded in the Explanatory Data Analysis group at LIACS.

**Skills & Abilities**
• Strong knowledge of and experience with machine learning, data mining, and statistics
• Knowledge of and experience with information theoretic learning (e.g., the MDL principle) is a plus
• Highly motivated to perform foundational data mining research and apply developed methods to real-world applications
• Creative, ‘making things work’ mentality
• Independent and communicative team player
• Experienced with writing scientific manuscripts
• Excellent academic writing skills
• Excellent programming skills (preferably in Python)
• Interested in contributing to educational activities
• Excellent proficiency in English (oral and written)

**Qualifications**
Required Degree(s) in:
• Computer Science
• Statistics
• Artificial Intelligence
• Data Science
• A related field (MSc degree required)

**Experience**
Other:
• Experience with machine learning, data mining, and statistics
• Experience with information theoretic learning (e.g., the MDL principle)
• Experience with writing scientific manuscripts

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