Postdoctoral Researcher in AI for Longitudinal and Population Health Risk Prediction

Postdoctoral Researcher in AI for Longitudinal and Population Health Risk Prediction

At the Vrije Universiteit Brussel, your impact reaches further than you think. Every role, be it as a researcher, academic, IT professional, or adm...

For more than 50 years, the Vrije Universiteit Brussel has stood for freedom, equality and solidarity, and this is very much alive on our campuses among students and staff alike.

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The Faculty of Engineering, DepartmentElectronics and Informatics (ETRO), research group Electronics and Informatics: Research – Development - Innovation, is looking for a postdoctoral researcher.

ETRO, the Department of Electronics and Informatics (http://www.etrovub.be/) of the Vrije Universiteit Brussel (VUB), conducts fundamental and applied research in micro- and optoelectronics, multidimensional signal processing, and audiovisual computing.

ETRO collaborates closely with UZ Brussel, a top-rated university hospital recognized nationally and internationally. We are a core member of imec, the world-leading research and innovation hub in nano-electronics and digital technologies. Our team is currently a fruitful mixture of people from different nationalities. The primary working language is English.

More concretely your work package contains:

We are seeking a full-time postdoctoral researcher to join an interdisciplinary research team working at the intersection of artificial intelligence, biomedical data science, digital health, epidemiology, and environmental health. The position focuses on the development, validation, and interpretation of AI models for health risk prediction using heterogeneous and longitudinal data sources.

The successful candidate will contribute to research on :

• Longitudinal patient-level prediction of disease progression, deterioration, and hospitalization;
• Population-level modelling of environmental exposures and health risks;
• Interpretable and uncertainty-aware machine learning for heterogeneous health data.

This position offers the opportunity to work in a multidisciplinary environment involving engineers, data scientists, clinicians, epidemiologists, public health experts, and project partners from academia, healthcare, industry, and research organizations.

Profile

We welcome candidates with a strong research background in machine learning, biomedical data science, biostatistics, epidemiology, medical informatics, environmental health, or a related discipline. Candidates should be motivated to develop AI methods and apply them to clinically or epidemiologically meaningful research questions.

We are particularly interested in candidates with experience in longitudinal data analysis, time-series modelling, clinical prediction, risk modelling, survival analysis, causal inference, spatio-temporal modelling, or multimodal data integration. The candidate should be motivated by applied research questions in health and be able to bridge methodological development and real-world biomedical and public health applications.

Responsibilities

You will be expected to:

• Develop, implement, and validate machine learning and statistical models for health risk prediction;
• Analyse heterogeneous health-related datasets, including clinical, longitudinal, environmental, demographic, socioeconomic, and geospatial data;
• Contribute to patient-level and population-level modelling approaches, including temporal, spatial, and multimodal prediction frameworks;
• Apply and evaluate methods for disease progression modelling, acute event prediction, hospitalization risk estimation, and health vulnerability assessment;
• Contribute to model interpretability, uncertainty estimation, fairness assessment, and validation procedures;
• Work with clinical, public health, environmental, and technical partners to translate research questions into well-defined computational studies;
• Prepare scientific pub