PhD Candidate Human-Centered Interpretable Machine Learning

**Job Description**
This PhD project focuses on developing human-centered interpretable machine learning models and algorithms. It aims to address the limitations of existing explainable AI methods by incorporating domain expertise through interactive learning and investigating hybrid models, such as combining pattern-based models with neural networks or learning from deep representations. The researcher will develop theory and algorithms for (hybrid) model selection based on the minimum description length (MDL) principle, with the resulting methods evaluated on real-world healthcare case studies to discover novel insights.

**Skills & Abilities**
• Strong knowledge of and experienced 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 and 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

**Experience**
Other:
• Experienced with machine learning, data mining, and statistics
• Experienced with writing scientific manuscripts

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