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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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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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Engineering and Systems Data Science / Computer Science , Machine Learning Appl Deadline: 2025/11/10 11:59PM (posted 2025/09/24, listed until 2026/03/24) Position Description: Apply Position Description Duke
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experience in areas that align with the faculty (https://neuroscience.barnard.edu/people ), specifically in the areas of vision, learning, and development at the molecular, cellular, systems, cognitive
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: Modelling of optimization problems mainly related to the indicated line of research, as well as the design, implementation, and validation of algorithms to solve them Where to apply Website https
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project, you will develop machine learning models that learn from high-throughput experimental datasets to uncover structure–property relationships and guide the selection of new experiments. The datasets
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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 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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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
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Engineering, or related field. Research experience with Artificial Intelligence/Machine Learning/Large Language Model. Publication track record in a series of top tier conference papers e..g, in NeuRIPS, ICLR