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techniques that are useful for the modelling of many real-life systems. These include the development and analysis of stochastic models, computer simulations, differential equations, statistical inference
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on preferences the candidates will work along one (or more) of the following different directions: theoretical foundation involving quantitative models (e.g. stochastic, timed weighted, hybrid automata) and logics
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. Your work will focus on developing physics-informed AI methods to enhance decision-making in design and operation of next generation thermal energy storage systems, such as latent heat TES and
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have the opportunity to design and conduct lab or survey experiments that reveal how people process economic information and form beliefs about future macroeconomics indicators. You'll have access
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involve close links to experimentalists with the chance to test out results at leading XFEL facilities in Europe/USA. Outcomes will include an enhanced understanding of stochastic processes like ion hops in
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MMF/Nexus pipeline and the stochastic Bayesian Bisous method. To improve, extend and deepen the analysis to a full dynamical inventory, a major incentive for the project is the application and
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their entire life cycle due to the variations arising from geometry, material properties and loads during the long-term operation. This leads to a growing need in model identification, calibration, and
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the SFF Integreat, The Norwegian Centre for Knowledge-driven Machine Learning (ML) , a centre of excellence funded by RCN and in operation until 2033. The project PI and team are also in close collaboration
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Stochastics and mathematical finance Discrete mathematics and optimisation Discrete geometry Numerical mathematics Applied analysis Mathematics for AI Course organisation The BMS programme is divided into two
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-refinement for finite element and scientific deep learning methods (gradient-free adaptation, generalized stochastic gradient descent methods) Salary for PhD students: is AUD32K per annum, tax free