法国国家信息与自动化研究所(INRIA)博士后职位—机器学习和信号处理

Research Engineer F/M in machine learning and signal processing

Inria

Description

Located at the heart of the main national research and higher education cluster, member of the Université Paris Saclay, a major actor in the French  Investments for the Future Programme (Idex, LabEx, IRT, Equipex) and partner of the main establishments present on the plateau, the centre is particularly active in three major areas: data and knowledge; safety, security and reliability; modelling, simulation and optimisation (with priority given to energy).

The 500 researchers and engineers from Inria and its partners who work in the research centre’s 30 teams, the 60 research support staff members, the high- level equipment at their disposal (image walls, high-performance computing clusters, sensor networks), and the privileged relationships with prestigious industrial partners, all make Inria Saclay Île-de-France a key research centre in the local landscape and one that is oriented towards Europe and the world.

Context

The successful candidate will work closely together with Alexandre Gramfort and Thomas Moreau, and become a member of the Parietal team at Inria-Saclay https: // team.inria.fr/parietal/.

This research project is funded by the French national research agency (ANR). All intellectual and data resources necessary enabling this project are provided by the Parietal team, which does not preclude fruitful exchange with collaborators of Parietal.

The candidate will benefit from the numerous developments of the Parietal team and its expertise in scientific computation applied to various domains (machine learning, optimization, statistics, neuroscience, medicine).

Assignment

The objective of this position is to consolidate the software stack of the team on deep learning for EEG/MEG signals especiallly on topics related to automatic data augmentation. See recent works:

https: // arxiv.org/abs/2106.13695

https: // arxiv.org/abs/2202.02142

Main activities

  writing reproducible code for dissemination of the work

  pro-active reading, staying on top of the latest research

  conducting data analysis and simulations

  communicating and presenting results at different stages of the work

Skills

Technical skills and level required:

  solid knowledge in data analysis and applied statistics

  solid working knowledge in scientific computing with Python or R

  expertise with MEG / EEG is a strong asset

Languages:

  excellent written and oral communication skills in English

Relational skills:

  strong social communication skills

  fast prototyping, frequent communication

  embracing errors as opportunities for learning, favoring output over perfectionism