PhD Projects/ DIAI

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

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 (6 starting in 2021, and 6 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.

Find out more about the selected projects for 2021 here.

Cosmologie – amas de galaxies – intelligence artificielle

Nicolas Cerardi ➔

Exploration intelligente de lames histologiques.

Zhuxian Guo ➔

Prediction of demographic indicators from remote sensing images

Basile Rousse ➔

Dark energy studies with the Vera Rubin observatory LSST & Euclid-developing a combined cosmic shear analysis with bayesian neural networks

Justine Zeghal ➔

Statistical and machine learning methods for survival data: prediction, performance assessment and interpretability

Ariane Cwiling ➔

Design principles of property graph languages

Alexandra Rogova ➔

Leveraging multivariate geophysical and geochemical time series for monitoring volcanic systems: can we use machine learning?

Matthieu Nougaret ➔

Learning the magneto-ionic side of the turbulence in the interstellar medium in radio-astronomy

Jack Berat ➔

Metamorphoses and optimal transport for the multimodal registration of brain tumor images

Guillaume Serieys ➔

Deeply Learning from Neutrino Interactions with the KM3NeT neutrino telescope

Santiago Pena Martinez ➔

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