Title of research project

IHU Prometheus G561

PHD

Contract type Contrat (CDD) Category Workload Fulltime

Scientific field Informatique

WorklJob Descriptionoad

Description of the offer This PhD project aims to design a multimodal and temporal multitask learning framework for early sepsis diagnosis and risk prediction. Sepsis is a life-threatening condition caused by a dysregulated immune response to infection, making early detection essential yet highly challenging due to heterogeneous symptoms and complex multimodal data (vitals, lab tests, imaging, and clinical text). The proposed research focuses on multitask learning (MTL), where several interrelated clinical tasks—such as risk prediction, severity classification, and biomarker identification—are learned simultaneously within a shared model. Building on transformer-based architectures, the work will develop methods to handle task interference and conflicting objectives through gradient-based bi-level optimization and task decomposition via representation learning. The model will also integrate temporal reasoning to capture sepsis progression and emphasize interpretability to ensure clinical relevance. Finally, it will be validated on large-scale datasets (MIMIC-VI and IHU cohorts) to evaluate robustness, generalization, and potential deployment in real-world healthcare systems.

Field of research Deep learning , AI for Health

Skills

Knowledge

Work environment

Location Villetaneuse

Laboratory Laboratoire d'Informatique de Paris Nord

Degree level

Degree required Master 2 en science des données

Experience required

WorkloadInformations complémentaires

Start date 01 01 26

Job Type Contrat (CDD)

Composition of the selection jury

(Président(e)) Hanane Azzag

Application deadline 12 01 25

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