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characterize the spatio-temporal contexts that favor crises. • Development of advanced predictive models (multivariate approaches, machine learning) combining event data, snow and weather data, and remote
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and temporal patterns from multisource data, spatiotemporal data analysis and mining and model learning and physical parameter prediction. Responsibilities consist of conducting computer modeling
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the medium and long term. We are looking for a Machine Learning Research Engineer: The ideal candidate will bring deep expertise in state-of-the-art deep learning methods applied to computer vision, 3D
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. The research in the PhD project will focus on core spatio-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation
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Inria, the French national research institute for the digital sciences | Paris 15, le de France | France | about 1 month ago
that diffusion models are a fundamental divergence from traditional deep learning paradigms. This suggests that existing generalisation theories are insufficient and highlights the need for a bespoke, algorithm
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factors. Though not required, we are particularly interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and
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water quality parameters and predict cyanobacteria blooms in the Tietê system reservoirs. Activities: 1. Develop machine learning models for estimating water quality parameters via remote sensing; 2
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engagement. This includes the development and deployment of large language model–based platforms to support learning and research, as well as enhanced digital and high-performance computing infrastructure
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systems; longitudinal health data integration; genomic and multi-omics data analysis; AI and machine learning for precision diagnostics; predictive modeling of treatment outcomes; biomedical big data
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to optimise built-environment thermodynamics and occupant comfort by creating predictive AI tools for spatiotemporal heat transfer. Machine learning algorithms will identify energy inefficiencies and propose