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Today, companies across a wide range of industries face the challenge of managing increasingly complex stochastic systems, where uncertainty is inherent and data is abundant. These systems arise in
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of stochastic programming problems used in power system operations under uncertainty in conjunction with classical high-performance computing. Interesting directions for this project include, but are not limited
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Management (WRM) and Earth System Change (ESC) at Wageningen University (WU), where you will work with experts on stochastic hydrology, agricultural water management and hydrological modelling. There is also
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it crosses over to a stable state. This project aims at identifying the quantities characterising the stochastic nature of this phenomenon, such as the average and the distribution of the random
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range of industries face the challenge of managing increasingly complex stochastic systems, where uncertainty is inherent and data is abundant. These systems arise in diverse domains—such as manufacturing
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the quantities characterising the stochastic nature of this phenomenon, such as the average and the distribution of the random transition time. Job requiements The successful applicant will have: Completed a PhD
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of this project is to develop techniques that will enable gate-based quantum optimization algorithms to tackle realistic (large-scale, mixed-integer, and constrained) instances of stochastic programming problems
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) for engineering systems and structures, as well as expertise in machine learning, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R
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, stochastic modeling, and Bayesian statistics. Programming Skills: Proficiency in programming languages such as Python, C, or R. Teamwork and Responsibility: Ability to work effectively within a project team