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motion and the viewing perspective of the observer (Nikolaidis et al, 2016). This project will develop continuous models of action legibility using these sources of information from data collected in a
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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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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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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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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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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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the areas of fluid dynamics, turbulence and net-zero combustion. There is substantial scope for the student to direct the project with the main focus on (i) Generating an advanced Direct Numerical Simulation
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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