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noise models, with particular relevance to the practical constraints of the NISQ era. This position offers not only the opportunity to pursue and shape an independent research agenda but also the
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learning, e.g. using JAX, based on numerical models such as Higher Order Spectral method, mcsimpy, etc. Collect real metocean data from relevant online databases, datastreams such as from R/V Gunnerus, and
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. using JAX, based on numerical models such as Higher Order Spectral method, mcsimpy, etc. Collect real metocean data from relevant online databases, datastreams such as from R/V Gunnerus, and experimental
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of physics-informed machine learning proxy models for large-scale CO₂ storage. The project addresses the significant computational burden of numerical simulation and optimisation of large-scale CO₂ storage
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numerical modelling skills (e.g. Python, MATLAB, CFD codes) Personal characteristics Flexible and dependable Collaborative and independent Innovative and open minded Strong analytical skills Emphasis will be
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expertise in nonlinear model predictive control. Expertise in numerical optimal control. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work independently
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approach of data-driven membrane discovery that includes material space construction and exploration, candidate selection and verification, providing data for machine learning models to optimise membrane
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of smart technologies to visualize yard operations in a digital form (such as virtual models and digital twins). Smart technologies can collect, analyze, and represent data from various sources
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numerical simulations from spectral wave and ocean models to produce high-resolution historical and future climate data. We encourage candidates with domain knowledge of wave modelling to apply, as
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. The link between graphite crystallinity, flake size, and purity (all being important for industrial applications) and its formation history is not fully understood. Therefore, the numerous graphite