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Your Job: The main objective of this PhD project is to achieve a better understanding of the efficient propulsion of trypanosomes through complex crowded environments, mimicking biological tissues
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that exhibit emergent turbulent behaviors, and (2) disordered optical media that process information through complex light scattering patterns. Using advanced imaging, machine learning techniques, and real-time
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excursions. The PhD student will work on analysing the reliability of paleomagnetic records, deriving uncertainty estimates, and building time-dependent models of the paleomagnetic field evolution using a
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and physics is curious, open-minded, a team player, and well organized has interest in modelling complex systems, ideally for particle radiation effects. Experience in these areas is therefore highly
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offered in this context, with the objective of modelling, coding, and field-validating a new mechanistic analysis tool for pavements containing fungal-bound granular layers. The research will focus on urban
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behaviour through these models using uncertainty quantification/machine-learning (UQ/ML) algorithms To optimise the manufacturing process with the help of the simulation tool To support in the development and
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PhD: Semantic Modelling of Geodata Sources and Geo-analytical Workflows Faculty: Faculty of Geosciences Department: Department of Human Geography and Spatial Planning Hours per week: 36 to 40
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into Question 2 of the Financial Details section. About the scholarship This PhD project will contribute to the development and optimisation of a Paediatric Post-Sepsis Model of Care that supports and improves
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 1 day ago
applications have been organized in different steps that interact with each other. The classical first step is to describe the application through a model. Through this model, a first process is operated
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address: Dr Fahad Panolan: f.panolan@leeds.ac.uk Project summary The Algorithms group at the University of Leeds (UK) is offering a fully funded 3.5-year PhD studentship on Parameterized Complexity and