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scientific knowledge, such as physical laws, differential equations, and domain-specific constraints, to model, simulate, and understand complex systems. The project will explore modern SciML methods
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resources efficiently. In this PhD project, you will develop mathematical theory and computational methods for the analysis and design of chaotic sampling mechanisms in networked control systems. You will
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, Mathematics, Engineering or a related discipline, with an interest in AI applied to complex and safety-critical systems. Good knowledge of Machine Learning and Deep Learning methods, including experience with
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Engineering, Bioinformatics, Statistics, Applied Mathematics, Physics, or a closely related STEM field. Demonstrated experience developing AI and machine learning models for biomedical applications. Job
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whether suitable relaxations of the underlying PDE constraints can effectively reduce the nonlinearity of the inverse problem. Understanding this requires mathematical analysis of the PDEs and the resulting
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: Completed, or soon-to-be completed MSc in the biological sciences or different fields in the natural sciences (e.g. computational, mathematical, earth or marine sciences) with a strong interest in ecology and
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systems. The mathematical disciplines involved are mathematical modeling, numerical analysis, and scientific computing. Your tasks will involve: Conducting research on uncertainty quantification in
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directions include: ● Generative models of cell–cell communication . Move beyond descriptive ligand–receptor analysis to models that predict and help understand how cells react to signaling cues. ● Joint
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explore new mathematical and computational approaches to represent such systems, drawing on ideas from stochastic processes, four-dimensional (space-time) modelling, complex systems, agent-based modelling
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The Department of Mathematics The Department of Mathematics strives for excellence in research. The department balances pure mathematical research with mathematical research motivated by