[et_pb_section fb_built=”1″ _builder_version=”3.22″][et_pb_row _builder_version=”3.22″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”4_4″ _builder_version=”3.0.47″][et_pb_post_title meta=”off” featured_image=”off” admin_label=”Titre de la page” _builder_version=”3.22.1″][\/et_pb_post_title][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”3_4″ _builder_version=”3.0.47″][et_pb_text module_id=”contenu” _builder_version=”3.22.1″ z_index_tablet=”500″]<\/p>\n
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\u00a0that 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).<\/p>\n
[\/et_pb_text][et_pb_toggle title=”Scope of scholarships” admin_label=”Call for proposals” _builder_version=”3.22.1″ z_index_tablet=”500″ locked=”off”]<\/p>\n
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\u00a012 PhD students<\/strong> (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.<\/p>\n Find out more about the selected projects for 2021 here<\/a>.<\/p>\n [\/et_pb_toggle][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″ locked=”off”][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/cosmologie-amas-de-galaxies-intelligence-artificielle\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n Nicolas Cerardi \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ transform_translate=”-4px|-4px” custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/exploration-intelligente-de-lames-histologiques\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-7px|-7px”]<\/p>\n Zhuxian Guo \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ transform_translate=”-4px|-4px” custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/prediction-of-demographic-indicators-from-remote-sensing-images\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-7px|-7px”]<\/p>\n Basile Rousse \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/dark-energy-studies-with-the-vera-rubin-observatory-lsst-euclid-developing-a-combined-cosmic-shear-analysis-with-bayesian-neural-networks\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n Justine Zeghal \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″ locked=”off”][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/statistical-and-machine-learning-methods-for-survival-data-prediction-performance-assessment-and-interpretability\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n Ariane Cwiling \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/design-principles-of-property-graph-languages\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n Alexandra Rogova \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/leveraging-multivariate-geophysical-and-geochemical-time-series-for-monitoring-volcanic-systems-can-we-use-machine-learning\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n Matthieu Nougaret \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/learning-the-magneto-ionic-side-of-the-turbulence-in-the-interstellar-medium-in-radio-astronomy\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n Jack Berat \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″ locked=”off”][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/metamorphoses-and-optimal-transport-for-the-multimodal-registration-of-brain-tumor-images\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n Guillaume Serieys \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/u-paris.fr\/diip\/deeply-learning-from-neutrino-interactions-with-the-km3net-neutrino-telescope\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n Santiago Pena Martinez \u2794<\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.22″ custom_padding=”0px||0px|”][et_pb_row custom_margin=”120px||” _builder_version=”3.22.1″][et_pb_column type=”4_4″ _builder_version=”3.0.47″][et_pb_divider _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”Also read” _builder_version=”3.22.1″]<\/p>\n [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”13″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.21″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|”][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" 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\u00a0that can cope with the scale… Lire la suite PhD Projects\/ DIAI<\/span><\/a><\/p>\n","protected":false},"author":35,"featured_media":0,"parent":1677,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"coauthors":[5],"class_list":["post-74","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/pages\/74","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/users\/35"}],"replies":[{"embeddable":true,"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/comments?post=74"}],"version-history":[{"count":26,"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/pages\/74\/revisions"}],"predecessor-version":[{"id":3454,"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/pages\/74\/revisions\/3454"}],"up":[{"embeddable":true,"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/pages\/1677"}],"wp:attachment":[{"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/media?parent=74"}],"wp:term":[{"taxonomy":"author","embeddable":true,"href":"https:\/\/u-paris.fr\/diip\/wp-json\/wp\/v2\/coauthors?post=74"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Cosmologie \u2013 amas de galaxies \u2013 intelligence artificielle<\/h3>\n
Exploration intelligente de lames histologiques.<\/h3>\n
Prediction of demographic indicators from remote sensing images<\/h3>\n
Dark energy studies with the Vera Rubin observatory LSST & Euclid-developing a combined cosmic shear analysis with bayesian neural networks<\/h3>\n
Statistical and machine learning methods for survival data: prediction, performance assessment and interpretability<\/h3>\n
Design principles of property graph languages<\/h3>\n
Leveraging multivariate geophysical and geochemical time series for monitoring volcanic systems: can we use machine learning?<\/h3>\n
Learning the magneto-ionic side of the turbulence in the interstellar medium in radio-astronomy<\/h3>\n
Metamorphoses and optimal transport for the multimodal registration of brain tumor images<\/h3>\n
Deeply Learning from Neutrino Interactions with the KM3NeT neutrino telescope<\/h3>\n
Also read<\/h2>\n