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in quantum control and quantum optics, including the ability to model and analyse light-matter interactions, dynamical evolution. Theoretical background in quantum channel discrimination and quantum
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of research output including publications, conference contributions and/or technical reports in the field. Theoretical background in quantum control and quantum optics, including the ability to model
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the ability to model and analyse light–matter interactions, dynamical evolution. Theoretical background in quantum channel discrimination and quantum metrology, with proficiency in applying information
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in quantum control and quantum optics, including the ability to model and analyse light-matter interactions, dynamical evolution. Theoretical background in quantum channel discrimination and quantum
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of research output including publications, conference contributions and/or technical reports in the field. Theoretical background in quantum control and quantum optics, including the ability to model
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/Computer Engineering, Computer Science, Applied Maths or related. Strong skills in AI techniques/ML/optimisation (Python/Matlab); familiarity with probabilistic modelling, time-series or control/power
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, computational modelling, and data-driven alloy design to: Understand the mechanisms of local austenite-to-ferrite transformation in low-alloy steels; Develop frameworks to predict and control
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data science methods to build explainable and integrated machine learning models that can be utilised by health services to make real-time, data-informed clinical decisions in youth mental health care
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Metallurgy and Corrosion cluster, working within a multidisciplinary team spanning theory, advanced characterisation, and computational modelling. This environment provides an excellent platform for developing
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models