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optimal transport and gradient flows to machine learning and optimization applications, such as deep generative models, sampling, inference, stochastic optimization, and beyond. The doctoral student will
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mathematics, Earth science, or a related discipline Skills in numerical modelling, programming, and handling large datasets Prior experience in machine learning is desirable Interest in nonlinear dynamics and
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 23 days ago
for automated content generation while maintaining pedagogical constraints, deploy targeted generative guidance aligned with established learning theories, and create compact student models for next-generation
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related to staff position within a Research Infrastructure? No Offer Description As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have
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fluorescence microscopy (SMLM, SIM), integrating physical-mathematical models, machine learning, and compressed sensing for accurate and efficient reconstructions. Applicants must submit a project implementing
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for collaboration. You will also have the opportunity to develop your own research project aligned to the interests of the MND group. This could include new machine learning models or exploring a particular aspect of
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machine learning to model network behavior from real-world measurements (e.g., [7]). Although promising, these approaches still face three major limitations: (i) they often rely on idealized and extensive
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. Into the second year, the project moves toward methodology refinement and Machine Learning integration. The student will execute a more ambitious cycle with a complex alloy system and integrate machine learning
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including antenna and/or microwave engineering * programming skills and working knowledge of Matlab programming environment * knowledge of mathematical modelling, machine learning and artificial intelligence
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. Research topics include: Development and validation of DORIS data processing and modeling Implementation of improved models for DORIS satellites and ground systems Cross-analysis of DORIS and other geodetic