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immunological datasets including flow cytometry, autoantibodies, circulating proteomic markers and gene expression data. You will also have extensive experience of machine learning techniques, and of leading
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Accountabilities Design and implement software pipelines to deploy ML models on edge devices with real-time inference capabilities. Optimize machine learning models (e.g., quantization, pruning) for edge hardware
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work on the development and implementation of machine learning models aimed at detecting urban drainage infrastructure components (such as stormwater drains, sewers, and manholes) from publicly available
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. Our team brings together deep expertise in software engineering, distributed systems, machine learning, and cybersecurity. We work across disciplines; from healthcare predictive models for urban air
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Team Leader, you will play an integral role in shaping a dynamic and practical learning environment. You will contribute to a wide range of student-led projects and research activities, involving
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for collecting and analysing small-volume blood samples. You will research and design advanced machine learning, AI and statistical methods to process and analyse data generated from microsampling, which may
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-grained simulations, advanced sampling, and machine learning for predicting and analysing short-lived protein conformations. Enhance and automate workflows for reproducible simulations and structure-based
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one of the following analysis techniques (multiple preferred): normative modelling, dimensionality reduction techniques, machine learning, deep-learning, state space modelling, advanced statistics
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search campaigns within MCC while also utilising the latest machine learning tools provided in platform to maximise campaign scale. In this role you will be the lead for paid search, and asked to plan your