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security mechanisms based on the intrinsic stochasticity and non-volatility of nano-devices. - Implementing simulation frameworks and experimental validation through lab characterization of devices and
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Rough paths, stochastic differential equations and stochastic PDEs Sub-Riemannian geometry The positions are full-time, fixed-term appointments, with an earliest start date on February 1st 2026. We Offer
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, including reinforcement learning and multiobjective stochastic optimisation. These methods will be applied to cooling solutions at the building, district, and city scale, where innovative and energy-efficient
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on the intrinsic stochasticity and non-volatility of nano-devices. Implementing simulation frameworks and experimental validation through lab characterization of devices and circuits. Designing and evaluating energy
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functional inequalities Rough paths, stochastic differential equations and stochastic PDEs Sub-Riemannian geometry The positions are full-time, fixed-term appointments, with an earliest start date on February
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has a strong background in control engineering, with documented expertise in optimal control, adaptive control and online optimization, stochastic systems, Bayesian inference, and state estimation and
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on aspects of fracture processes, stochastic material behavior, and the development of physics-based models that can bridge the gap between continuum mechanics descriptions and disorder-driven statistical
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on aspects of fracture processes, stochastic material behavior, and the development of physics-based models that can bridge the gap between continuum mechanics descriptions and disorder-driven statistical