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Field
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We, the 202Q-lab, are looking for a highly motivated post-doctoral researcher to develop travelling wave parametric amplifiers (TWPAs) based on the nonlinear kinetic inductance of superconducting
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for representing a large class of nonlinear systems by a structure close to the linear case or defined by a set of linear submodels [Bernal, 2023, Lendek, 2010]. This representation facilitates the performance
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on advanced nonlinear optics and ultrafast laser techniques. The experimental work will be carried out on a table-top HHG beamline, producing XUV pulses with durations in the tens-of-femtoseconds range
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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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optimization and applications to large scale problems, nonlinear preconditioners in high performance computing, computational mechanics, the PETSc toolkit, or PDE-constrained optimization. Any project is
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particular, we will use topology and shape optimisation methods to compute the optimal domain shapes that can stabilise solutions with desired/prescribed properties. We will use methods from inverse problems
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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
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Operator Algebras, Nonlinear Functional Analysis, Topology and Geometry, algebra, etc. l applied mathematics, including Partial Differential Equations, Dynamical Systems, Combinatorics and Graph Theory
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how DNA LLM work, and develop solutions to integrate them into the neural network architectures developed by the lab. - Focus on developing new solutions for the scalability of neural networks and large
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recommend that you apply early as the advert may be removed before the deadline. This fully funded PhD position, offers an exciting opportunity to develop and optimize multiscale models for surfactant