Call for Internships Master 2 or equivalent (A)
This year, after the usual call in autumn, DATAIA is launching a second wave of the call at the beginning of 2021.
Internship proposals will involve the collaboration of at least two people from two DATAIA partner institutions (see the list in Appendix 2 of the text of the call) who do not belong to the same laboratory or to the same host institution. The internship topic must fit in one of DATAIA subject areas (see Appendix 1 of the text of the call).
Deadline : 12 February 2021 at noon (Paris time)
Notification of decisions : 26 February 2021
Proposition (2 pages)
The partners describe the objective of the internship, its link with DATAIA, and list previous collaborations of the partners (if any). It is not essential to already have a candidate to submit the project.
If the project is accepted, DATAIA will fund the student for 4 to 6 months at the usual grant level (approximately 550 euros per month).
The internship report should be sent to DATAIA at the end of the internship. A short presentation may be requested during DATAIA's annual scientific days.
The application must include :
- Names of the host laboratories and project leaders and their contact information
- Names of laboratories (or teams) of the convergence institute that will benefit from the funding
- Names, contact details of the scientific and administrative managers within each laboratory (team) of DATAIA partners receiving funds
- Length of the stay and estimated arrival and departure dates of the internship
Host laboratories must send applications by email to: firstname.lastname@example.org
For any questions, please contact: email@example.com
directed by Onofrio SEMERARO, Lionel MATHELIN (LIMSI, CNRS) and Michele Alessandro BUCCI (Inria)
The objective of the internship is to explore deep reinforcement learning (DRL) methods in the search for optimal control strategies in the context of fluid dynamics.
directed by Florent BOUCHARD, Matthieu JONCKHEERE, Frédéric PASCAL (L2S, CentraleSupélec) and Alexandre Gramfort (Inria)
The internship aims at formulating and solving a source separation problem under a hypothesis of ellipticity on the considered random variables, thus generalizing classical Gaussian models, and at applying the proposed method on electroencephalography data.
directed by Frédéric CHAZAL, Jisu KIM (Inria) and François COKELAER, Mathieu FERAILLE (IFPEN)
The proposed topic aims at implementing deep neural network approaches for the prediction of the permeability index of a rock from high-resolution 3D tomographic images, in particular by integrating features from Topological Data Analysis (TDA).
directed by Cédric MEHL, Thibault FANEY (IFPEN), and Michele Alessandro BUCCI, Marc SCHOENAUER (Inria)
The objective of the internship is to implement and evaluate a method based on the concept of "Physics Informed Neural Networks" (PINNS) (which consists in integrating physical a priori in the learning of a neural network) to solve a problem of reactive fluid mechanics.