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socioeconomic impacts of artificial intelligence (AI), gender bias in algorithms, critical analyses of fintech, dynamics of platformisation, the emergence of digitally enabled forced labour, biometrics and
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require metrics that predict how well a given hearing aid algorithm will perform for a specific user in a particular acoustic environment. Existing approaches often rely on oversimplified assumptions about
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and Durham University. The primary focus will be on designing and implementing deep learning and anomaly detection algorithms to analyse large-scale, real-world sensor data collected from in-service
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the areas of Data Science and Informatics as defined by the Bayes Centre and Huawei. Strong background and expertise in LLMs, edge systems, deep learning algorithms and computer systems. Experience and
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programmes. The Computer Science programme is focused on software engineering, with modules in Software Engineering, Software Project Management, Data Structures and Algorithms and Data Base Systems. The
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Machine Learning for both supervised and unsupervised algorithms. Deep understanding of principles and best practice in machine learning, with a focus on NLP especially in sequence labelling tasks based
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algorithm, duality theory, and sensitivity analysis; Optimisation, focusing on single-variable optimisation, constrained optimisation involving non-linear objective functions with multiple variables, and the
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publications is a plus. Experience in designing, developing, and implementing computer vision models and algorithms. Proficiency in Python and its standard coding practices and common libraries. Experience with
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approaches, sound art, algorithmic governance, media and cultural studies, digital media, environmental approaches, and others. The communities and physical research sites of the project are located in
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transmission methods (wired or wireless) will be optimised for robust data capture in natural sleep environments. AI-Driven Analysis: Develop advanced AI algorithms to analyse the collected sensor data, aiming