Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
and with the 2AT team at Institut Pprime to develop an innovative jet-noise prediction tool. The researcher will develop a novel jet-noise prediction tool based on a resolvent analysis of the Navier
-
Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
-
AI researchers from ANITI, IMT and CERFACS, as well as with researchers/engineers in weather forecastings from the CNRM (Météo-France). Hybridization methods between neural networks and physical models
-
and difficult to anticipate. Predicting the spatio-temporal dynamics of these changes generally relies on empirical models, most of which project correlational relationships between species occurrences
-
Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning
-
prediction models, and visualizing immense volumes of various types of data, generated by agri-robots and IoT devices. The most popular classes of autonomous agricultural devices include: weeding robots
-
anticipating crises. Current landslide prediction models, based mainly on rainfall thresholds, become ineffective in the presence of snow cover. Snow acts as a temporary reservoir, storing precipitation before
-
close coordination with project partners, the recruited researcher will conduct experiments to determine the extent to which neural models, now at the heart of many approaches to Natural
-
analysis) to compare brain responses with predictions of computational models (deep neural networks developed by the NASCE team). The objectives include assessing how the brain segments, groups