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for the monitoring and prognostics of complex systems, characterized by high heterogeneity, nonlinear dynamics, and operational uncertainty. The main objective is to enhance the ability to promptly detect anomalies
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Application deadline: 30/06/2026 Research theme: Applied Mathematics, Continuum Mechanics, Nonlinear PDEs How to apply: https://uom.link/pgr-apply-2425 UK only due to funding restrictions. The
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aims to develop hybrid quantum–classical approaches for modeling multiphase flows governed by complex, nonlinear dynamics across multiple scales. The postdoctoral researcher will investigate how
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computational techniques. In particular, we are looking for curious, enthusiastic and hard-working candidates with the following expertise: -- strong understanding of concepts in nonlinear waves and fluid
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PostDoc/Senior Scientist - Process and Plant Design in the Field of Liquid Organic Hydrogen Carriers
languages (e.g., Python, Julia, Matlab) Strong interest in process modeling and simulation, including novel methods and approaches such as neural networks and nonlinear optimization Ability to analyze complex
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diffraction tomography, with a focus on inverse multiple-scattering algorithms. Implement and evaluate both linear approximation models and nonlinear high-order scattering approaches for accurate imaging
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to the lack of generation inertia worsening power system stability. Control of such a complex system relies on detailed understanding and real-time modelling of the nonlinear dynamics resulting from
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mathematics and simulations, applied mathematics modelling, nonlinear science, wave propagations in lattices, fluid mechanics and financial mathematics. About You The successful candidate will be expected
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improvements. Examples include optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques
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, 100% funded PhD student position to fill starting around June 2026. Research is to be in the field of computational methods in nonlinear and large scale optimization / inverse problems or in novel