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. The project builds on a recent work by the host group [Keijsers et al., Nature Photon. 19, 733 (2025)], where signatures of a phase transition were observed in the nonlinear response of a laser-driven optical
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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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of fluid mud. The project combines mathematical model development, nonlinear dynamical systems analysis, turbulence modelling, and environmental fluid mechanics. The successful candidate will contribute
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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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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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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
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coding for classical optical channels, such as low-power schemes for data-centre interconnects or code constructions for nonlinear fibre transmission. The role is deliberately broad within the FEC space