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.

Many data science problems, for instance in health or business, start from relational data, whether it is in explicit relational databases or in a set of tables. The data are not a numerical table, and an important part of the statistical modeling consists in crafting a variety of transformation to turn it into numerical vectors: discrete elements are one-hot encoded –though high cardinality needs more sophisticated encoding (Cerda and Varoquaux, 2020); information may be assembled across multiple tables, joining and aggregating on common entities. For instance, a good prediction of housing prices requires assembling various information about the neighboorhood –the access to education, transportation, parks, job, shops– more global trends of geographical growth... This information is available spread across multiple source, for instance on multiple internet pages. Crafting all the transformation required to turn these information in numerical vectors requires many manual data preparation steps and is arguably the number one time sink in data science.

In the soda team, we are adapting to relational data modern representation learning tools –those behind the deep learning revolution–, with the specific goal of learning vectorial embeddings of all the information in a database and thus greatly facilitate data preparation for data science.

The soda team is a newly created team doing research at the intersection between machine-learning, databases, and quantitative social sciences (eg empirical economy, epidemiology…). It hosts the team developing scikit-learn at Inria. The team has access to multiple large compute nodes with GPUs, an internal compute cluster, as well as the Jean Zay large supercomputer with GPUs.

Assignments : The recruited person will work under the direct supervision of Gaël Varoquaux.

Collaboration : The work will be done within the subgroup of soda working on automating preprocessing and analysis of relational data: 2 engineers, 2 students (soon 3), and wider collaborations with experts on NLP, knowledge bases, and deep learning such as Alexandre Allauzen, Fabian Suchanek, and Edouard Oyallon.

For a better knowledge of the proposed research subject : a detailed scientific description of the research program is available on https://team.inria.fr/soda/job-offers/ . The output of our previous research project on dirty-data is available on https://project.inria.fr/dirtydata/publications/ .

Main activities :

• Design and validate deep learning architectures to capture the information in relational data
• Experiment on data-science tasks to understand the benefits brought by these architecture
• Write publications explaining these progresses

Additional activities :

• Help supervise students and engineers
• Collaborate with data scientists to understand the challenges in a variety of applications (such as health or socio-economic questions)
• Release software demonstrating the methods developed

Technical skills and level required : understanding of the workings of deep learning will be valued

Languages : English is the only required language, however a good abilty to writing clear, didactic scientific publications is important.

Relational skills : kindness and enthousiasm make happy teams.

Other valued appreciated : Curiosity, and a desire to learn.

• 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

2653 €/month (gross salary)