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often fail to preserve the fidelity of combined datasets, leading to loss of crucial information. This proposal aligns directly with the CAMS Data Analytics Theme and the Grand Challenge of using machine
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, microbiology, environmental science, chemistry, physics or data science. Applications would be keen to blend hands‑on experimentation with advanced analytics to create low‑carbon, nature‑based solutions
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, while simulations are subject to error due to uncertainty in nuclear data and unresolved physical processes e.g. thermal expansion and fine-scale inhomogeneities. Generating independent simulation
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. Experience of advanced data analytics, including if possible experience of coding in Matlab and/or Python, is highly desirable. Experience of collecting neuroimaging and/or wearables data from infants/children
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project will involve collecting and analysing EEG, fNIRS and home wearable recordings from babies and children. Experience of advanced data analytics, including if possible experience of coding in Matlab
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, fNIRS and home wearable recordings from babies and children. Experience of advanced data analytics, including if possible experience of coding in Matlab and/or Python, is highly desirable. Experience
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Primary supervisor - Prof Kate Kemsley Join us to research and develop advanced analytical methods for tackling food fraud head-on! Economically motivated adulteration of foods is a significant
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experience of data science analytical packages and R or Python would be advantageous. Funding The project is funded by the UKHSA and Nottingham University Business School. As a UK public body, UKHSA can only
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to the analysis of time series. In particular, the project will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show
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will examine and develop methods that go beyond the Markovian paradigm. It will consider a range of time series data, focusing on those that show challenging properties of uncertainty, irregularity and