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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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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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Research theme: Chemometrics The project is jointly funded by the Community of Analytical Measurement Sciences and the University of Manchester Department of Chemistry. The successful candidate will
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. In-depth knowledge of hemodynamics, particularly in the context of coronary artery disease and cardiovascular flow phenomena. Strong analytical skills and the ability to interpret complex simulation
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on electrochemical sensing, bioelectrode development, and analytical performance enhancement. Position Requirements Required Qualifications M.Sc. degree in Analytical Chemistry, Electrochemistry, Biochemistry
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machine learning frameworks like PyTorch. The candidate should demonstrate strong analytical abilities, an eagerness to learn and solve complex problems, and possess effective collaboration skills
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. Comparison with known analytic methods and established Markov models will be made wherever possible. Expected outcomes include a unified non-Markovian framework for time series analysis, a suite of relevant
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Economics, to develop next-generation analytic tools that will illuminate the lived experience and care pathways of people with Lewy Body dementia (LBD). Each studentship is fully funded (stipend €25,000 p.a
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neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection. Comparison with known analytic methods and
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have detailed expertise in analytical and spectroscopic tools, namely NMR, XRD, IR, UV-Vis, MS and very good language skills in English and/or German. Please send your application (motivation letter, CV