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, machine-learning model development, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful applicant must
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qualification; (ii) demonstrate research skills in imaging science and machine learning, particularly on image reverse engineering, fake image and video detection, statistical detection models and mathematical
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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measurement technique development, atmospheric modelling, and advanced methods for integrating observational and model data through data assimilation and machine learning. About the research project The overall
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implement machine learning models dedicated to the prediction, interpretation, and quantitative analysis of Raman vibrational spectra, establishing explicit links between structure, local chemical environment
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Social Science / Machine Learning / Data Science would be a plus Experience of organising and conducting a variety of quantitative and qualitative research techniques and methods Skills &Competencies
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protein foods including through high moisture extrusion. Key responsibilities will include: Explore innovative methods for food process optimization including the use of AI and machine-learning Develop and
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explicit model of the biophysical effect of land use change, a machine learning emulation of dynamic global vegetation models. Both activities aim to improve understanding and quantification of the effects
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with experimentalists to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers
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languages. Have experience of quantitative analysis, computational modelling, bioinformatics, machine learning, or another form of data-intensive academic research. Have a strong interest in interdisciplinary