2022-05592 - Post-Doctoral Research Visit F/M Distributed Machine Learning, from ultra-low-power devices to Edge
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

Contexte et atouts du poste

The context of this position is a partnership with Orange and Freie Universität Berlin (TinyPART project) as well as with the RIOT community, around the topic of experimental low-power AI and TinyML. The goal is to explore multi-scale Machine Learning, applicable not only in the cloud and at the edge, but also on microcontrollers.

Mission confiée

Missions :
Design, develop and evaluate experimental Machine Learning software libraries exploiting low power connected objects and the thing-edge-cloud continuum.

For a better grasp on the targeted topics :
See practical literature such as TinyML [1] for inference on microcontroller, as well as some of the recent existing research articles on learning on microcontroller (such as [2][3]), low-power federated learning (such as [4]), and keynotes on the topic such as [5]. Also see embedded ML software bases such as those proposed by EdgeImpulse, TensorFlowlite-micro, etc., and embedded software platforms for 32-bit microcontrollers (especially RIOT [6]).

[1] T. Warden, D. Situnayake, "TinyML", O'Reilly, 2019..
[2] MCUnetv3 https://tinytraining.mit.edu/
[3] H. Ren et al. "TinyML with Online-Learning on Microcontrollers" Proceedings of IJCNN, 2021
[4] MM Grau et al. "On-Device Training of Machine Learning Models on Microcontrollers With a Look at Federated Learning." ACM GoodIT, 2021.
[5] C. Adjih, "Machine Learning for IoT", Workshop on IoT and Emerging Technologies, 2022.
[6] RIOT operating system for low-power IoT.

Principales activités

Main activities :

  • Review state of the art for on-device learning on microcontrollers;
  • Explore, design, develop and evaluate modifications of engines for dynamic execution of inference models, such as:
    • Modifications for dynamic execution of inference model (eg layer skipping and early exiting of existing models);
    • Life-cycles modifications for on-board inference models;
    • Thing-edge-cloud continuum (distributed systems) modifications to partly distribute inference and learning (towards federated learning);
  • Academic research publications in the field of AI on constrained devices, thing-edge-cloud continuum and TinyML.

Complementary activities:

  • Upstreaming of open source code (e.g. in the RIOT ecosystem);
  • Contributions to standardization (e.g. IETF).


Collaboration :
Some trips/stays in Berlin may be realised in this context, thanks to our collaboration with Freie Universität Berlin on this topic.


Technical Skills and Level Required:
- Knowledge and proficiency in Machine Learning techniques;
- Knowledge and mastery of Python, and low-level C programming language and tools;
- Knowledge of low-level software optimization techniques, e.g. on 32-bit microcontrollers.

Languages & Interpersonal skills :
- Good command of scientific English;
- Teamwork (partially geographically distributed).


  • 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