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Field
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Automated Program Repair (APR) is the grand challenge in software engineering research. Many APR methods have shown promising results in fixing bugs with minimal, or even no human intervention
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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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the construction of PRS and enhance disease prediction. Students will gain experience in: Statistical genetics and GWAS methodology Machine learning approaches for high-dimensional data Algorithm development and
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Application Deadline 18 Nov 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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2 x full time, 3-year fixed term positions, located on the Camperdown Campus at the School of Mathematics and Statistics Opportunity to drive research in computational pure mathematics through
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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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aims to leverage quantum computing to address these challenges, focusing on developing novel quantum algorithms to enhance mRNA sequence design You will be co-supervised by leading experts in AI, data
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formative assessment and personalised feedback while ensuring fairness, accountability, and transparency. The research will explore a combination of algorithmic design, human–AI interaction, and empirical
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algorithms for computing MML solutions beyond the one-dimensional case. Extend existing dynamic programming approaches to higher-dimensional problems or develop novel approximation methods that preserve
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Performance . About You The successful candidate will play a key role in the development and validation of computational tools that integrate spatial transcriptomics, algorithmic methods, and machine learning