Design engineer for EUREKA BuddyBeWell project (M/F)

European Commission; France; Sur place; EUR 30 000 - 45 000; Plein temps

Résumé du poste

A leading research institution in France is seeking a researcher for the EUREKA BuddyBeWell project. The role focuses on developing multimodal machine learning systems for emotion detection from audio and text, aimed at supporting elderly users. Candidates should possess a Master's degree in computer science and strong skills in machine learning and automatic language processing. This position offers a unique opportunity to work on innovative robotics aimed at enhancing the quality of life for older adults.

Qualifications

• Strong skills in machine learning, both supervised and semi-supervised.
• Experience in automatic language processing essential.
• Ability to work independently and in a team.

Responsabilités

• Implement multimodal machine learning approaches for emotion detection.
• Collect data and supervise emotion annotations.
• Assess ethical dimensions of human-robot interactions.

Connaissances

Supervised machine learning

Semi-supervised machine learning

Deep learning

Automatic language processing

English proficiency

Formation

Master's degree in computer science or similar

Description du poste

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Organisation/Company CNRS Department Laboratoire Interdisciplinaire des Sciences du Numérique Research Field Engineering Computer science Mathematics Researcher Profile Recognised Researcher (R2) Country France Application Deadline 18 Aug 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2025 Is the job funded through the EU Research Framework Programme? Horizon 2020 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

This position is in the field of natural language and speech processing. The successful candidate will join the international EUREKA BuddyBeWell project. He/she will implement multimodal machine learning approaches with LLMs for emotion detection from audio and text. He/she will be able to use an initial corpus of human-robot interactions collected in Woz and annotated on emotions. He/she will have to complete this first corpus with a second collection of data from elderly people. The protocol will be submitted to the Saclay University ethics committee. A protocol for validating interactions with Buddy will also be written and submitted to the ethics committee. A prototype will be built as part of the project. An initial phase of testing will be carried out internally at LISN. We will try to have a second phase of continuous testing in a home for the elderly. Research into the ethical dimensions of these interactions will be carried out.

- Participate in the project and collaborate with the project's various research teams.
- Collect data in Woz and supervise emotion annotations in a residence for the elderly.
- Build a multimodal audio and text-based emotion detection system for elderly voices.
- Test Buddy by following the validation protocol internally at LISN, then if possible externally in an elderly people's home.
- Assess the ethical dimensions of Human-Robot interactions

This position is part of the international EUREKA BuddyBeWell project funded in France by BPI. This robotics project aims to develop ways of helping people age in good health. Many negative effects on (mental) health can be mitigated by lifestyle interventions that promote active, positive and social living. Social robotics could be the key to effective implementation of these interventions. Evidence from recent literature reviews on the impact of social robotics on the psychological well-being of older people is encouraging. The aim of this project is to provide elderly users (and their carers) with a robotic companion featuring the latest artificial intelligence technologies. The robot will provide personalized advice on healthy living, enabling people to stay at home longer.

- Master's degree in computer science, automatic language processing or similar.
- Skills in supervised and semi-supervised machine learning, including deep learning and LLMs.
- Experience in automatic language processing
- Good command of English, both written and spoken
- Ability to work independently and as part of a team
- Ability to prioritize tasks and take initiative.