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identify the necessary solutions. The findings should also remain valid under different climate change scenarios. With its global state-of-the-art energy system model, ICE-2 at Forschungszentrum Jülich
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the formation rates or composition of a biominerals from known environmental conditions. This project aims to construct such a model. The ultimate goal is to create a general framework for predicting
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metocean and biogeochemistry models accounting for climate change impacts across Europe (from the Nordic Seas to the Black Sea and Mediterranean Sea) and the Pan-Arctic region. The PhD project focuses on new
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://github.com/FZJ-IEK3-VSA/RESKit ). This framework currently uses historical weather data to model energy output and will be further developed to allow the simulation of renewable electricity production under
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materials. The computer modelling of LSP remains challenging due to its multi-physics and multi-scale nature. The dependency of the process on the shape of the laser pulse, its energy, ablation layers etc. is
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characterization, and integration of machine learning to correlate synthesis conditions with functional performance. The goal is to establish predictive synthesis strategies for oxygen vacancy control, with
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Martin Australia invite applications for a project under this program, exploring the development of Physics Informed Neural Networks (PINNs) for efficient signal modelling in areas such as weather
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sound background in geophysical fluid dynamics, experience in numerical ocean or atmospheric modelling, and experience with numerical data analysis. Good scientific presentation, writing, and
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| £20780 + £2500 industry top up (per annum (tax free)) Overview This exciting, fully-funded PhD opportunity invites applications from candidates with a robust foundation in data science, modelling, and
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invites applications from candidates with a robust foundation in data science, modelling, and/or engineering, and a keen interest in deploying data analysis and artificial intelligence (AI) to solve real