IN-DEEP, an MSCA Doctoral Network project aimed at training doctoral students in Deep Learning techniques, kicks off
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The initial meeting (Kick Off meeting) took place on February 1st.
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The project is coordinated by researcher David Pardo (UPV/EHU), with coordination from BCAM led by Postdoctoral Researcher Judit Muñoz Matute.
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IN-DEEP's objective is to enhance training and research in new deep learning technologies for inverse problems
IN-DEEP, a MSCA Doctoral Network project for training PhD students in Deep Learning techniques, has been launched. The kick-off meeting took place on February 1st. The aim of IN-DEEP is to enhance training and research in new deep learning technologies for inverse problems.
The project is coordinated by researcher David Pardo (UPV/EHU), with coordination from BCAM led by postdoctoral researcher Judit Muñoz Matute.
IN-DEEP is endowed with 2.3 million euros to train and supervise 9 highly qualified doctoral students through a consortium of universities and companies from different research areas and sectors within the European Union.
The project will focus on real high-risk problems derived from applications related to geophysics, smart cities, and health. IN-DEEP will conduct fundamental research in universities and research institutes that will be validated and applied to real cases in technological centers and companies. IN-DEEP offers the opportunity to train PhD students to become excellent researchers in DL techniques for inverse problems fundamental to our society, with a comprehensive profile and suitable professional prospects in both academic and non-academic sectors.
During the kick-off meeting, topics such as the agenda for the first 24 months, the structure and functions of the network, and communication tools were discussed, among many other things.
