PhD in Data- and AI-based Solutions for Child Health and Housing
Develop data-driven AI models to optimize housing upgrades for children's health and wellbeing.
Best for:Best for quantitative researchers with a strong background in data science, Python programming, and an interest in public health and urban systems.
Overview
Eindhoven University of Technology is offering a four-year PhD position focused on leveraging big data and artificial intelligence to evaluate and improve children's health through housing interventions. Working within the Urban Systems and Real Estate group, the candidate will transform complex unstructured housing and health datasets into predictive models and a practical digital tool. The project forms part of the NWO-funded 'Homes for Upcoming Generations' programme in collaboration with medical centers, municipalities, and industry partners. The successful applicant will conduct interdisciplinary research, complete advanced doctoral training, and contribute up to 15% annually to teaching activities.
What you'll do
- Design and execute independent PhD research combining housing, public health, and artificial intelligence.
- Analyze large-scale, complex, and unstructured housing and health datasets using advanced statistical methods and machine learning.
- Develop predictive models and a digital decision-support tool to assess the impact of housing improvements on child health outcomes.
- Collaborate closely with an interdisciplinary team including medical researchers, social scientists, engineers, and municipal partners.
- Participate in doctoral courses, seminars, and international conferences for professional development.
- Contribute to departmental teaching tasks for up to 10% to 15% of the employment time.
Requirements
Degree required: MSc in Exact or Data Sciences, Quantitative Economics, or Engineering
Degree offered: PhD
Language: English; strong spoken and written proficiency
Instruction: English
- Proven programming experience in Python
- Experience working with big data and quantitative data analysis
- Strong educational background in subjects such as calculus, linear algebra, computational modelling, econometrics, or data science
- Strong analytical skills and problem-solving abilities
- Demonstrated ability to bridge fundamental and applied research
Funding & terms
How to apply
Deadline: 2026-11-15 · open until filled
Candidates must submit their application online through the university portal using the apply button. Applications sent via email or post will not be processed. Ensure all requested documents, including a motivation letter and curriculum vitae with references, are included.
- Cover letter
- Curriculum vitae
- List of publications
- Contact information of three references
Contact
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