Data Intelligence Institute of Paris (diiP)
PhD Scholarships on Data Intensive Artificial Intelligence

Presentation of the DIAI project

Extracting knowledge from the data means that we need to perform analysis tasks that are becoming increasingly complex as the amount of data, the number of observed variables, and the levels of noise (e.g., when measuring weak signals) in the measurements grow. Therefore, we are in need of novel methods that can cope with the scale of data and complexity of tasks that we face across applications in different domains. In this context, we need to develop new techniques that will advance the state of the art in the areas of data analytics, data science, and data intelligence (including artificial intelligence, machine learning, deep learning).

Scope of scholarships

These scholarships, which are proposed in the context of the 2019 Data Intensive Artificial Intelligence (DIAI) project that is co-funded by ANR, will support 12 PhD students (8 starting in 2021, and 4 in 2022), working on topics related to the intersection of data management/data analytics and machine learning/artificial intelligence, in order to address fundamental interdisciplinary challenges related to data analysis in modern science, industry, and society. Topic proposals should explain their relevance to the above dimensions.

The doctoral schools (Ecoles Doctorales – ED) participating in the DIAI project and relevant to this call are the following:

ED 127 (AAIF): topic proposal deadline May26
   for details, contact the ED at ecole-doctorale.astro[at]obspm.fr

ED 130 (EDITE): topic proposal deadline Apr15 at 12 noon ; full application deadline May19 at 12:00 noon
   to submit proposal, use this template, send email to: diai.ed130[at]listes.u-paris.fr

ED 386: full application (topic+candidate) proposal deadline ~Jun15
   for details, contact the ED at ufr-ed386[at]math.univ-paris-diderot.fr

ED 560 (STEP’UP): topic proposal deadline Apr16
   to submit proposal, follow ED procedures, email to all 3 addresses below:
edstepup_pu[at]univ-paris-diderot.fr
scol-ed[at]ipgp.fr
diai.ed560[at]listes.u-paris.fr

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