Scientific Programmer in Privacy-Preserving Federated Learning

il y a 2 semaines


Villeneuved'Ascq, Hauts-de-France INRIA Temps plein

About INRIA and the MAGNET Team

INRIA is a leading French research institute dedicated to advancing computer science and its applications. The MAGNET team, based in Lille, focuses on developing innovative solutions for privacy-preserving machine learning, particularly in the context of federated learning.

The Role: Scientific Programmer

We are seeking a talented Scientific Programmer to join our team and contribute to cutting-edge research projects in the field of privacy-preserving federated learning. You will play a key role in developing algorithms, tools, and platforms that enable secure and privacy-preserving machine learning applications.

Project Context: TRUMPET, FLUTE, and REDEEM

The MAGNET team is actively involved in several European research projects, including TRUMPET, FLUTE, and REDEEM. These projects explore the potential of federated learning for various applications, with a particular focus on oncology.

Your Responsibilities:

  • Design and develop algorithms for privacy-enhancing technologies (PETs) in the context of federated learning.
  • Contribute to the development of open-source platforms and libraries for secure and privacy-preserving machine learning.
  • Collaborate with researchers and engineers to integrate PETs into existing machine learning workflows.
  • Conduct experiments and benchmark algorithms to evaluate their effectiveness and efficiency.
  • Stay up-to-date on the latest advancements in privacy-preserving machine learning.

Required Skills:

  • Strong understanding of distributed algorithms and machine learning concepts.
  • Proficiency in software development, particularly in Python and/or C/C++.
  • Experience with process models and probabilistic reasoning techniques.
  • Familiarity with programming language internals (e.g., abstract syntax trees).

Benefits of Joining INRIA:

INRIA offers a stimulating and collaborative research environment, competitive compensation packages, and opportunities for professional development.



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