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communication journals Demonstrable proficiency in advanced quantitative data analysis: applied machine learning, statistical analysis, and handling complex data. Programming skills in Python and R are essential
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manufacturing. You should have interest in or experience with data-driven methods, including machine learning, Python programming, or data curation. Regular reports of research progress are required and research
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10 minutes and machine learning algorithms to deliver quantitative diagnosis without destroying the samples. The AF-Raman prototype will be integrated and tested in the operating theatre
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time role, 0.1FTE. The activities of this role will support development of future proposals for funding, into AI for renewable energy. You will consider ways in which the integration of machine learning
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for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. The post-holder will also be responsible for writing up the findings
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, Bayesian and maximum likelihood approaches, spatial statistics and random forests or other machine-learning approaches and be quick to learn new techniques. Enjoyment of analysis of large and spatially
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machine learning. Supervision will be provided by Prof. Ali Mazaheri, as well as Prof. Fang Gao Smith, and Prof. Helen McGettrick. The successful candidate will have a strong background in psychology
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smart cities. To be successful you will have: PhD in Computer Science, Data Science, Machine Learning, Electrical Engineering, or a related field with focus on speech processing, edge computing, TinyML
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quantitative and digital methods, such as descriptive/inferential statistics, data modelling, machine learning (ML), experimental prototyping and technology ideation. A significant degree of autonomy is required
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of this postdoctoral research position is to support the design of elastic aircraft with a combination of aerodynamic tools of different fidelity, and the development of machine learning technologies in the context