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Field
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of an object or medium from measurements of waves that have propagated through it. Mathematically, such problems lead to large-scale nonlinear inverse problems governed by partial differential equations (PDEs
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part of a new lab at the Delft Center for Systems and Control, supervised by Gabriel de Albuquerque Gleizer. The research will allow you to gain deep insights across nonlinear dynamics, optimization, and
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tunneling diode (RTD) excitable dynamics with quantum-dot active devices capable of providing a nonlinear amplifying response to extremely weak optical stimuli. These photonic neurons will be realized as
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quantitative properties of an object or medium from measurements of waves that have propagated through it. Mathematically, such problems lead to large-scale nonlinear inverse problems governed by partial
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inspired by notions of energy, leading to highly efficient, local learning rules. What are you going to do? As a PhD candidate, you will develop mathematical theory for learning in nonlinear and dynamic
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The Max Born Institute for Nonlinear Optics and Short Pulse
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
nonlinear optimal control algorithms. Experimentally validating the proposed methods on a waste-sorting robotic platform. Contributing to mechanical and mechatronic design choices whenever they influence
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imaging systems. These methods naturally lead to challenging mathematical models, including nonlinear partial or ordinary differential equations. However, real-world optical systems are subject to
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(ILM), interacting with PhD students, postdocs, and master's students on related topics. We seek a motivated candidate holding a master's in physics, with skills in ultrafast/nonlinear optics or soft
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nonlinear behavior makes it difficult to guarantee safety and stability. How can we exploit the expressive power of machine learning without compromising the rigorous guarantees required in safety-critical