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learning, artificial intelligence, statistical computing, data visualisation, computational statistics, or data science, who are excited to contribute to our world-class research, innovative teaching, and
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with generous top-up scholarships. We're looking for talented students with a background in mathematics, computer science, statistics, economics, engineering or other related fields. These positions
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This project involves the automated generation of textual descriptions for audio content, such as spoken language, sound events, or music. This process typically employs deep learning techniques, such as recurrent neural networks, transformer models, and so on, to analyse audio signals and...
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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ideas through implementation science and health research The Opportunity The School of Nursing and Midwifery has two exciting Research Fellow positions available to contribute to the ENGAGE Project
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, artificial intelligence, statistical computing, data visualisation, computational statistics, or data science, who are excited to contribute to our world-class research, innovative teaching, and growing
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placements at the forefront of sustainable chemistry, catalysis, and advanced manufacturing. Current available projects are: Mechanochemical Synthesis of Single-Atom-Catalysts (SMC) (Faculty of Science
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PatchSentinel-X: Transformer-Based Security Patch Intelligence for Vulnerability Lifecycle Assurance
++, Java, or Python code, Basic static analysis tools such as Semgrep, CodeQL, or SonarQube Nice to Have CodeBERT, CodeT5, or GraphCodeBERT, Program analysis, Data-flow and control-flow understanding
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knowledge program analysis, fuzzing, software testing, natural language processing
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-performance computing and geochemical modelling to decipher atmospheric CO2 removal mechanisms over geological timescales. The research involves integrating surface process simulations with tectonic frameworks