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of reinforcement learning (RL) for industrial process control and optimisation. The research focuses on developing RL methods that can support decision-making and control in complex industrial systems while
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journals. To a limited extent, your appointment will also involve coordinating and supervising exercise sessions, laboratory sessions, and project-based learning activities associated with courses taught by
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. Knowledge of data analysis, optimization, and machine learning techniques is a plus. Knowledge of machine learning libraries (e.g., PyTorch or TensorFlow), SDR hardware (e.g., USRP) and software (e.g
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. The research may also examine the growing role of influencers and content creators as news providers within digital information environments. It may further investigate whether and how people learn about current
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learning, with the aim of controlling the vessel in an optimal and efficient manner. In this context, control is formulated as a trade-off between different objectives, such as minimising energy consumption
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well as biologists. This in turn requires complete honesty and ease in revealing which fields the candidate is not an expert in, such that other team members can teach and support them Desirable: Having taken courses
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to quality, integrity, creativity and cooperation. You have a profound knowledge of wireless communications, networking, and signal processing. You have at least intermediate knowledge of machine learning
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PhD in Computational Simulations of Turbulent Reaction Flows for Clean Energy and Sustainable Propul
machine-learning methods, you will analyze flame-turbulence interactions, pollutant formation, as well as unclosed terms relevant to LES modeling. The analysis involves the fluid dynamic as
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PhD pathway. You will present the findings of your PhD research at academic conferences and you will publish them in scientific journals. Education You will provide support with teaching and also teach
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, engineered powders, including Cermetal and WC-Co-based materials, will be investigated as energy-absorbing media within the damping system. The development combines computational modeling, machine learning