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Full-time onshore enrolment Strong background in fluid-structure interaction or in systems and control Solid background in mathematics (theories in both ordinary and partial differential equations
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pulses develop numerical codes to calculate the system dynamics by solving partial differential equations (e.g. Schrödinger equation, von Neumann equation) model the coupling to lattice vibrations (i.e
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engineering, or a related discipline Experience in mathematical modelling and numerical methods for ordinary and partial differential equations Strong interest in working in a cross-disciplinary, collaborative
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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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that the programme will combine ideas from a broad range of disciplines, including machine learning, control theory, differential equations, port-Hamiltonian systems theory, modelling of power systems, digital signal
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. The specific research areas we will explore are + Adaptive scientific deep learning methods for mathematical physics problems governed by partial differential equations (domain decomposition, adaptive quadrature
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aspects include rough paths and subsequent developments for nonlinear stochastic partial differential equations. The theory of signatures and rough volatility also provides important connections to algebra