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develop a versatile time series foundation model that can capture complex temporal patterns and adapt to various downstream tasks such as anomaly detection [2] and classification [3,4]. The ultimate goal is
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extraordinary ideas - and the people who discover them The Opportunity We are seeking a highly motivated Research Fellow to join the Faculty of Science and School of Physics and Astronomy to develop methods
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Mixed-Integer Programming (MIP) solvers are very powerful tools to solve combinatorial problems that arise in many industries. Modern MIP solvers usually run a sequence of algorithms to solve
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appropriate technique for their software system. In this project, we will develop AI-based methods for selecting the appropriate APR technique to fix a particular bug. The aim is to improve the effectiveness
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package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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automated recovery algorithms, improving system resilience. Research Areas for Master’s and PhD Students AI-Enhanced Resource Forecasting and Optimization: Research Focus: Developing and testing ML algorithms
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applications for monitoring individuals' health, activities and social behaviour. Yet, there is a lack of common and standard guidelines in developing mobile sensing apps (from both software development and UI
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-tracking, pupillometry), cognitive modelling, and regulatory analysis to assess how algorithmic explanations shape human judgement and how existing legal and ethical frameworks align with the evolution
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This project aims to develop robust algorithms capable of identifying and analyzing fingertips extracted from both static images and video footage. Machine learning techniques, particularly computer