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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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cultures, used to study human–pathogen interactions and epithelial barrier function in diseases such as COPD and cystic fibrosis. The digital twin, which is a computational model running alongside the ALI
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double) PhD program between Ghent University and their home university. Students selected under this preference program will be exempted from paying tuition, bench fees, or any other study-related costs
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Programme) and starting on 1 October 2026, you will prepare a PhD dissertation. Context. Climate change is increasingly leading to periods of extreme drought and intense rainfall. Soil compaction exacerbates
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academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and
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of the exercise sessions for: Business Informatics Business Information Systems You will contribute to the daily functioning of the department. For this function, our Brussels Humanities, Sciences & Engineering
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consistent dataset for the systematic study of nationalist agitation. Computational methods - such as GIS mapping, network analysis, and corpus linguistics - allow researchers to identify patterns and systemic
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), Prof. Maarten Dewilde and colleagues are developing next-generation technologies to transport biological therapeutics across the BBB. To strengthen our growing research program, we are seeking a highly
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neurodegeneration, and affinity with student and PhD supervision, with a collaborative spirit, to join our team in Leuven. Required Qualifications PhD in Bioinformatics, Computational Biology, Systems Biology, or
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intersection of oncology, epidemiology, medical informatics and AI-assisted data curation. The project offers the opportunity to extend the work beyond UZA into the international OHDSI oncology evidence network