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on the innovative principle of powder charging and acceleration in a customised electrostatic field. The ultimate objective is to design, test and propose a new, more flexible and scalable metal additive
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emphasis on machine learning applications in asset pricing and corporate finance, alongside traditional econometric and factor modelling techniques. The successful candidate will play a pivotal role in
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emphasis on machine learning applications in asset pricing and corporate finance, alongside traditional econometric and factor modelling techniques. The successful candidate will play a pivotal role in
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focused on algorithm design and software development, especially in its early stages, we also intend to employ the methods developed for such applications as electrolyte decomposition in metal-ion batteries
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will involve data collection, conducting exploratory analysis, developing prediction model, developing transport optimisation models and supporting the project management framework development. PD1