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will consider techniques like flow matching, and use ideas from optimal transport and neural (stochastic) differential equations, invariant Kalman filtering and geometric numerical integration
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the potential to generate a stochastic gravitational-wave background. The work will focus on conformal extensions of the Standard Model with non-Abelian gauge symmetries consistent with neutrino oscillation data
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tasks. Contract period: August 15–December 31, 2026. Where to apply Website https://www.uniovi.es/conocenos/rrhh/convocatorias/investigacion Requirements Research FieldEconomicsEducation LevelMaster
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systems with boundary reservoirs, including through generalizations of the Matrix Product Ansatz method; (ii) studying stochastic duality properties and algebraic structures associated with Markov processes
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connection with uncertainty or data science. Specific fields of interest include, but are not limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty
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connection with uncertainty or data science. Specific fields of interest include, but are not limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty
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on facility location, storage, processing capacity, procurement, and inventory management. The project combines: stochastic and robust optimization, uncertainty modelling, large-scale supply chain design, and
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integrated circuits within the scope of the project, with a particular focus on stochastic computing and implementing hardware-efficient neural architectures. Their responsibilities will include RTL design
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limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty quantification, model order reduction, data assimilation, optimal control, and their applications
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of interest include, but are not limited to, stochastic partial differential equations, optimal transport, gradient flows, uncertainty quantification, model order reduction, data assimilation, optimal control