Building a Shared AI Care Intelligence Layer
Duration: 6 months
Location: Lissi laboratory of University Paris-Est Créteil UPEC, France
Level: Master 2 in Data Science, Artificial Intelligence, or Computer Science
About the Project
The project aims to improve care coordination for older adults across different care settings, including nursing homes (EHPADs), healthcare and social care facilities, and home-based care.
The project addresses the fragmentation of care information across medical documents, care plans, caregiver observations, and communications between families and professionals. It aims to develop a Shared AI Care Intelligence Layer combining Large Language Models (LLMs), multimodal AI, and specialized AI agents to integrate care information, reason across time, identify meaningful changes, and support coordinated human intervention.
Internship Objectives
The intern will contribute to the design and development of this AI intelligence layer, focusing on:
- Integrating and structuring heterogeneous care information using LLMs and multimodal AI.
- Developing a shared, longitudinal care memory to track observations, needs, and interventions over time.
- Implementing temporal reasoning and change detection to identify meaningful changes in care needs.
- Developing specialized AI agents to support care-plan synthesis, follow-up, and coordination between family caregivers and professionals.
- Evaluating the accuracy, reliability, and usefulness of the proposed AI system.
Candidate Profile
Required skills:
- Strong programming skills in Python.
- Good foundations in machine learning and deep learning.
- Familiarity with LLMs, Transformers, and NLP.
- Experience with data processing and model evaluation.
Preferred skills:
- Experience with LangChain, LangGraph, RAG, or AI agent frameworks.
- Knowledge of multimodal AI, knowledge graphs, or temporal reasoning.
- Interest in healthcare AI and digital health.
Expected Outcomes
The intern will contribute to a prototype of the Shared AI Care Intelligence Layer for improved continuity and coordination of care across home-based and institutional settings.
Application
Interested candidates should send their CV to alice.othmani@u-pec.fr , a brief motivation statement, and links to relevant projects or GitHub repositories, if available.
Please note that this internship may lead to a CIFRE PhD (industry-funded doctoral position in France) or an R&D engineer position for candidates who demonstrate strong performance, commitment, and dedication throughout their internship.
