Offre d'emploi
Centres Inria associés
Type de contrat
Contexte
<p>The position will be supported by FedMalin, a collaborative project on Federated Learning between 11 teams at INRIA. The project addresses FL challenges when deployed over the internet (privacy, heterogeneity, energy, fairness, ...) and has medicine as a main targeted application domain.<br /><br />FedMalin develops several software tools, including the open source library DecLearn (https://gitlab.inria.fr/magnet/declearn/declearn2) for private and decentralized/federated machine learning and data analysis. The hired engineer will contribute to the ongoing development of DecLearn, expanding its capabilities with new algorithms and enhanced functionalities.<br /><br />The activities will include interactions with the members of the project, the Magnet and Premedical teams (researchers and engineers). We also expect to conduct multi-centric medical studies across several hospitals. The activities can also include travel, e.g., to conferences to demonstrate the developed library and to contribute to the community building effort.</p>
Mission confié
<ul>
<li>Consolidate and extend the existing library for decentralized and privacy-preserving machine learning developed in the project</li>
<li>Deploy the library in real-world conditions and experiment on synthetic and (benchmark) medical data, analyzing the benefits and the costs compared to a centralized approach.</li>
<li>Publish open source code and integrate in existing libraries</li>
<li>Publish scientific results in medical and computer science conferences</li>
</ul>
<p>The Declearn project is available at https://gitlab.inria.fr/magnet/declearn/declearn2</p>
<li>Consolidate and extend the existing library for decentralized and privacy-preserving machine learning developed in the project</li>
<li>Deploy the library in real-world conditions and experiment on synthetic and (benchmark) medical data, analyzing the benefits and the costs compared to a centralized approach.</li>
<li>Publish open source code and integrate in existing libraries</li>
<li>Publish scientific results in medical and computer science conferences</li>
</ul>
<p>The Declearn project is available at https://gitlab.inria.fr/magnet/declearn/declearn2</p>
Principales activités
<ul>
<li>Implement federated and privacy-preserving algorithms for machine learning</li>
<li>Experiment with medical partners on multicentric medical studies</li>
<li>Evaluation of results</li>
<li>Reporting, disseminating and presenting results</li>
</ul>
<p> </p>
<li>Implement federated and privacy-preserving algorithms for machine learning</li>
<li>Experiment with medical partners on multicentric medical studies</li>
<li>Evaluation of results</li>
<li>Reporting, disseminating and presenting results</li>
</ul>
<p> </p>
Compétences
<ul>
<li>Programming skills in Python, including object oriented programming, unit testing, documentation writing, deployment tools, asynchronous programming and networking.</li>
<li>Good understanding of scientific papers on machine learning.</li>
<li>Interest for machine learning and medical applications. </li>
<li>Good communication skills; communication and animation of software development communities, git workflow</li>
</ul>
<li>Programming skills in Python, including object oriented programming, unit testing, documentation writing, deployment tools, asynchronous programming and networking.</li>
<li>Good understanding of scientific papers on machine learning.</li>
<li>Interest for machine learning and medical applications. </li>
<li>Good communication skills; communication and animation of software development communities, git workflow</li>
</ul>
Référence
2025-09065
Domaine d'activité