PhD position: Integration of Adaptive Causal Learning into Federated Continual Learning for Personalized Homecare Monitoring
PhD Proposed Start Date: October 2026
Required profil:
The ideal candidate holds a Master's degree (or equivalent) in one or more of the following disciplines: computer science, biomedical engineering, applied mathematics, or a related quantitative field. The following competencies are sought:
- Solid foundations in deep learning, Federated Learning, Continual Learning, Causal Inference, etc.
- Proficiency in Python and relevant libraries (PyTorch or TensorFlow, scikit-learn, transformers, etc.).
- Experience or strong interest in Edge Computing.
- Capacity for interdisciplinary collaboration with clinical partners.
- Scientific writing skills and ability to communicate results to both technical and clinical audiences.
Short abstract:
This PhD project aims to develop an Adaptive Causal Federated Continual Learning framework for personalized homecare monitoring. The proposed approach will enable AI models to learn collaboratively from decentralized and privacy-sensitive patient data, adapt continuously to changes in health conditions, devices, and lifestyles, and preserve previously acquired knowledge. By integrating causal representation learning, the framework will distinguish medically meaningful relationships from spurious correlations and provide interpretable explanations for clinical decision-making. The resulting system will be evaluated on temporal healthcare datasets and validated with clinical experts to ensure personalization, robustness, privacy compliance, and clinical relevance.