R&D Engineer - Distributed/federated machine learning over IoT data streams

R&D Engineer - Distributed/federated machine learning over IoT data streams

Level of qualifications required: Graduate degree or equivalent

Other valued qualifications: PhD in Computer Science

Function: Temporary scientific engineer

Level of experience: From 3 to 5 years

Context

The MiMove team at Inria Paris (https://mimove.inria.fr) undertakes research addressing the whole lifecycle of next-generation distributed systems, from their conception and design to their runtime support, focusing on mobile systems. We develop solutions at the intersection of distributed systems and software architectures, particularly middleware solutions. MiMove works on these topics through many national and international collaborations with academia and industry, including large-scale software development of real-world systems. MiMove’s research results impact various application domains, focusing particularly on IoT and smart cities.

Inria MiMove is one of the 7 partners of the CP4SC (Cloud Platform for Smart City) project (https://eviden.com/industries/public-sector-and-defense/cloud-platform-for-smart-cities) funded by the France 2030 program. The industrial CP4SC platform coordinated by Eviden/Atos aims to enable a data space that will collect data from IoT sensors and other data sources and will support data analytics for addressing the needs of national and local governments regarding the development of energy, mobility, and environmental policies.

Assignment

In this context, the R&D Engineer will be the main contributor for Inria MiMove to the CP4SC project. The objective will be to develop a solution for distributed/federated machine learning over IoT data streams.

Main activities

Continuous data streams from distributed sources raise several challenges compared to ML inside a static centralized data repository. Furthermore, running the ML process on top of distributed/federated resources and/or near the data sources can be complex but advantageous in managing computational complexity, reducing network communication, and respecting data confidentiality.

The R&D engineer will introduce, design, and implement such a solution as part of one or more use case scenarios of the CP4SC project while considering the requirements from these use cases.

In a second step, we aim to enhance our solution with Automated Machine Learning (AutoML) features. AutoML leverages algorithms and computational capabilities to automate key aspects of the ML pipeline, such as feature engineering, model selection, and hyperparameter tuning.

Additionally, the R&D Engineer is expected to further support MiMove’s research in relation to the identified and other related topics.

Skills

The candidate should have either a PhD in Computer Science or a Master’s degree with additional experience in research projects. Expertise is required, including experience in the implementation of related software prototypes, in one and possibly several of the following topics:

• Machine learning
• Distributed systems
• Internet of Things
• Cloud/edge computing

Besides English, a good level of spoken and written French will be a plus.

Benefits package

• 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 and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural, and sports events and activities
• Theme/Domain: Distributed Systems and middleware
  System & Networks (BAP E)

Instruction to apply

Defence Security:
This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST). Authorization to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision regarding a position situated in a ZRR would result in the cancellation of the appointment.

Recruitment Policy:
As part of its diversity policy, all Inria positions are accessible to people with disabilities.

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Location: Paris, FR

Posted Date: 11/17/2024