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Field
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knowledge of energy system modelling or climate modelling Good knowledge of deep learning, PDEs or mathematical/numerical optimization methods Enthusiasm for challenging problems and interdisciplinary
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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, Applied Mathematics, or a related field. Strong foundation in computational modelling & numerical simulations The laboratory The Decision and Bayesian Computation (DBC) – Epiméthée (EPI) laboratory
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Numerical simulations of Lattice QCD DoS Dr. Craig McNeile (craig.mcneile@plymouth.ac.uk , tel.: +441752586332) 2nd Supervisor Dr. Vincent Drach ( vincent.drach@plymouth.ac.uk , tel: +441752586335
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to date by validating numerical models against test data, before undertaking parametric studies to investigate the sensitivity of the key variables that affect the flexural performance of composite steel
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research identifies an active and growing research field, with numerous advancements in the past 18 months. A focus on generative AI agents has progressed capabilities towards exploiting zero-day
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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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that you contribute to the development of the Renovation Digital Twin concept (led by Saxion). Essential activities within your project will be to: · model data standards for design of renovations
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Wikibase instance Curate and model historical migration datasets within the dedicated Wikibase instance Contribute to the design of ontologies and metadata schemas for the knowledge graph Develop data-driven
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with colleagues at DTU and IIT Bombay, as well as with academic and industrial partners globally. The main purpose of this PhD position is to develop, implement and assess machine learning models