**Job Description**
The PhD candidate will join an interdisciplinary team to understand and predict how people engage with digital health interventions using AI and machine learning. The primary goal is to make digital health interventions more effective by predicting and improving adherence. This involves developing AI-based predictive models to anticipate user engagement and behavior, primarily utilizing real-world sensor and app data from unobtrusive measurements. The research will construct an AI-driven framework that predicts intervention use and behavior, applying it to various health-promoting use cases, including data from lifestyle interventions to reduce dementia risk and web-based interventions for STD prevention.
**Skills & Abilities**
• Strong interest in digital interventions, health promotion and behavior.
• Affinity with data science (e.g., complex statistics, machine learning or computational modelling) or willingness to develop relevant skills.
• Affinity with health promotion or willingness to develop in that field.
• Knowledge in programming (e.g. Python, R, SQL) and data science frameworks (TensorFlow, PyTorch, Scikit-learn) is a plus.
• Excellent English language skills.
**Qualifications**
Required Degree(s) in:
• University Master’s degree in a relevant field concerning health (e.g., health science, health psychology)
• University Master’s degree in a relevant field concerning data analysis (e.g., data science, statistics)
**Experience**
Other:
• An (almost) completed University Master’s degree.
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