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Description The majority of hydrological models rely heavily on the principle of mass balance, often represented through Ordinary Differential Equations (ODEs). These models encapsulate
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PhD/postdoc fellows) to carry out experimental projects with an EMBL host research group, thereby allowing them to build new networks, experience the international and interactive environment at EMBL
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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project
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Lazzarini as your proposed principal supervisor, and copy the link to this scholarship web page into question two of the financial details section. About the scholarship Diabetes is the most rapidly growing
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challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
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physics, microbial ecology, plant nutrition, plant physiology, plant ecology, biochemistry, and/or bioinformatics Strong interest in using process-based mathematical modeling to simulate biogeochemical
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mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent
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to the development of multiscale computational models for simulating crack propagation and establishing reliable methods to predict the residual strength of composite structures. The simulations, performed in Ansys
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Modern numerical simulation of spray break-up for gas turbine atomisation applications relies heavily upon the use of primary atomisation models, which predict drop size and position based upon
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PhD position on Modelling of Ocean Alkalinity Dynamics Faculty: Faculty of Geosciences Department: Department of Earth Sciences Hours per week: 36 to 40 Application deadline: 30 August 2025