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database (e.g., relational database) to store key workforce and service data developing reproducible workflows and code for extracting, transforming and loading (ETL) data into structured formats designing
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it is imperative that samples are promptly received and processed in a timely and rigorous manner, and all data and methodology is recorded in an organised and precise manner electronically
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first-opinion practice. As part of this project, you will gain mixed-methods research experience, including data collection and analysis using both quantitative and qualitative methods. Prior experience
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business partnering function of the finance team Helping to strengthen controls, improve resilience and ensure timely and accurate financial information and advice to budget holders and other stakeholders
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with expertise in areas such as Machine Learning and Deep Learning, Generative AI, Data-Centric and Responsible AI, or AI for real-world applications (e.g., health, cybersecurity, robotics, or business
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research on this topic. Projects have been designed to generate tangible outputs such as published papers, software or data sets within a 12-month period. The fellowship should not be viewed as an
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will also work alongside other team members to examine patients’ responses by analysing EEG and behavioural data. Candidates are expected to hold a medical qualification (MBBS or equivalent) and be fully
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-quality, repeatable data. Develop and apply signal processing workflows for weak transient signals, long-term monitoring, feature extraction and statistical analysis. Support sound playback/stimulation
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ability to write clean, well-documented analysis code. Ability to work with EEG data and apply multivariate methods to extract meaningful measures relevant to memory and consolidation. Skilled in developing
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information about the role, please contact Adele Kelly, Faculty Operations Manager, via email on a.kelly@mdx.ac.uk Middlesex is committed to fostering an inclusive working environment. We encourage applications