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Field
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environmental, climate and economic domains. It applies economics to these challenges, formulating problems in a context of uncertainty, making the most of weak data and signals, and applying dynamic, stochastic
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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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for stochastic, distributionally robust, and mixed-integer nonlinear optimization problems. The successful candidate will conduct research at the intersection of stochastic programming, optimization under decision
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project advances this work by incorporating the effects of stochastic variability in neutron loading, blanket geometry, and microstructural material evolution resulting from irradiation. These factors
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LIP - Laboratório de Instrumentação e Física Experimental de Partículas | Portugal | about 2 months ago
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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programs is possible including algebra, analysis, combinatorics, control theory, dynamical systems, geometry, numerical analysis, probability, statistics, stochastic analysis/control, and partial
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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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valued: nonlinear dynamics, condensed-matter physics, and stochastic processes. The ideal candidate is an experimentalist who enjoys not only setting up and performing complex experiments, but also
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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