The MSCA COFUND SOUND.AI programme aims to foster excellence in training, mobility and career development for doctoral students in one of the most dynamic AI communities in the world.

From 2023, the SOUND.AI project will implement a highly selective programme open to 30 internationally recruited PhD students. It draws on the scientific and strategic policy of Sorbonne University and its partners to actively develop fundamental and applied projects in three key interdisciplinary areas:

The implementing partners are Sorbonne University, MNHN, UTC, Insead, Inria, IRD, Inserm and CNRS. Already associated within the framework of the Sorbonne University Alliance since 2012, they actively support education through research and pedagogical innovation, and the creation of experimental multidisciplinary programs.

The associated partners complement each other to form a rich ensemble: first, through extra-academic partners whose cross-disciplinary expertise naturally fits into the three disciplinary areas. They also provide training on subjects in which they specialize: APHP (health), cultural data (BnF), artistic creation (Ircam), renewable energies (IFPEN), AI systems certification (LNE), the City of Paris (outreach).

Second, international partners in Europe (ELLIS, CLAIRE, UCL) and North America (MILA, UC Berkeley, OBVIA) allow us to offer an extremely wide range of partnerships to the laureates, whether for co-supervision, international mobility, or participation in training.

Finally, the complementary nature of our industrial partners is also a vital element to the research and training of fellows. Their diversity - large groups (EDF, Suez, Essilor, Pierre Fabre, SIEMENS, Valeo), SMEs (CRITEO, Naver Labs), start-ups (Datacraft, Hugging Face, OpenClassrooms) - will show the laureates different professional cultures, visible through training activities or secondments. 


Some important dates:

01/10/2022 - 30/11/2022: Collection of PhD research topics from partners

01/12/2022 - 31/01/2023: Call for applications open for PhD candidates