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tasks and meet deadlines rigorously. [assessed at interview] Desirable criteria Familiarity with neo4j, python, Flask and software containers. [assessed at interview] Familiarity with machine learning and
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strong analytical and problem-solving skills. A background in machine learning, data science, automation, optimisation, or control is desirable. You will have experience in analysing data with machine/deep
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A Machine Learning Enabled Physical Layer for 6G Radio Systems
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) improve the estimation method using information from the first part of the work and additional constraints, including a Machine Learning approach. (3) inspect how the mismatched expected and measured
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subsystems • High performance tunable and reconfigurable oscillators and frequency synthesisers • Application of AI / Machine Learning to physical layer circuitry, signals and waveforms Researchers can expect
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various applications. Potential applications include environmental monitoring, process manufacturing, machining, scientific characterisation, and renewable energy systems. The outcomes of this research will
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and has a large group of collaborators. You will be joining a great team of supportive and social PhD students working in a high-quality research environment. Learn More: The Dynamics Research Group
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-based models (based on machine learning, agent-based, etc) primarily to: i. predict energy demand in multi-energy systems (airflow, electricity, heat) ii. dynamically manage building systems to maintain
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The Centre for Doctoral Training in Machining, Assembly and Digital Engineering for Manufacturing (MADE4Manufacturing CDT) is a collaboration between the Faculty of Engineering, Advanced
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themes of “digital research”. The research elements of the post will include the application of big data approaches, artificial intelligence and machine learning to neurodegenerative disease drug design or