Post-Doctoral Research Visit F - M Operationalization Of Federated Learning For Healthcare Applications H/F INRIA

Post-Doctoral Research Visit F/M Operationalization of federated learning for healthcare applications
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Contrat renouvelable : Oui

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

Niveau d'expérience souhaité : De 3 à 5 ans

A propos du centre ou de la direction fonctionnelle

The Inria centre at Université Côte d'Azur includes 42 research teams and 9 support services. The centre's staff (about 500 people) is made up of scientists of dierent nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regiona economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

Contexte et atouts du poste

In the context of the ANR funded project Fed-Ops we are seeking a Postdoctoral Researcher focusing on the operationalization of federated learning (FL) in sensitive healthcare applications. This position will address critical challenges such as data integration and continual learning, ultimately contributing to the development of innovative methodologies and tools for federated learning. The project's outcomes will be integrated into the open-source framework Fed-BioMed and demonstrated in an existing federated learning infrastructure linking multiple Cancer Centers.

Mission confiée

- Literature Review: Conduct a comprehensive review of current research in federated learning, specifically focusing on its applications in healthcare. Identify gaps in existing studies.
- Algorithm Development: Design and implement novel federated learning algorithms tailored for healthcare data, addressing challenges such as data heterogeneity, privacy concerns, and communication efficiency.
- Data Management: Collaborate with healthcare institutions to acquire and manage diverse healthcare datasets while ensuring compliance with data protection regulations.
- Mentorship: Supervise graduate students or interns working on related projects, providing guidance in research techniques and methodologies.

Principales activités

- Data Integration: Develop methodologies for coherent data analysis and standardization from heterogeneous medical information including medical imaging and text records. This will involve:
Organizing and mapping data expressed in natural language within textual documents.
Creating automated, scalable Extract, Transform, Load (ETL) procedures utilizing Large Language Models (LLMs) suitable for federated environments.
Developing innovative methods for federated analytics, allowing for statistics and visualization of complex multi-centric data.
- Continual Learning: Operationalize continual learning practices within a federated learning context. Responsibilities will include:
Defining novel metrics for quantifying the quality of federated models in production.
Investigating optimal methods to assess and account for clients' heterogeneity and data changes in a federated setting.
Exploring federated unlearning mechanisms to ensure compliance with GDPR and uphold the "right to be forgotten."
- Collaboration: Work closely with a multidisciplinary team of researchers, healthcare professionals, and technology developers to ensure the applicability and effectiveness of the developed methodologies. Supervision of interns and students, and presentation of the results in relevant scientific and clinical venues.

Compétences

Required Skills

- Ph.D. in Computer Science, Data Science, Biomedical Engineering, or a related field, with a focus on machine learning or data integration.
- Strong background in medical imaging, federated learning and artificial intelligence in medical applications.
- Proficiency in programming languages such as Python, R, or similar, with familiarity in data visualization tools and libraries.
- Demonstrated ability to publish research findings in peer-reviewed journals and present at conferences.
- Excellent problem-solving skills, critical thinking, and a passion for utilizing technology to improve healthcare outcomes.
- Experience working in collaborative, interdisciplinary environments.

Preferred Skills

- Knowledge of GDPR and ethical considerations in healthcare data usage.
- Experience with Large Language Models (LLMs) and ETL processes in health informatics or similar fields.
- Understanding of clinical workflows and medical data standards (e.g., HL7, FHIR).

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage

Rémunération

Gross Salary: 2788 € per month