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such as network analysis in 3D genome regulation that integrate diverse biological information, including transcription factor (TF)–DNA interactions, epigenetic features, and 3D genome organization
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the correctness, robustness and reliability of deep neural networks and AI-enabled software systems. Job Responsibilities: Develop novel methods and algorithms for the verification, testing and robustness analysis
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methods, data mining, and machine learning methods. Documented shell script, R, Python, and C programming skills. Documented experience in big data analysis and computational tool/package development
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such as network analysis in 3D genome regulation that integrate diverse biological information, including transcription factor (TF)–DNA interactions, epigenetic features, and 3D genome organization
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for integrating large language models (LLMs) with empirical data analysis to enhance threat understanding, uncover emerging attack patterns and evaluate real-world security risks. Collaborate with academic and
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of methods for tasks such as data harmonisation, phenotype representation, genomic analysis and patient or gene prioritisation. Working within research high-performance computing (HPC) environments
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and AI-enabled threats using large language models (LLMs), artificial intelligence techniques, and empirical security analysis methods. Support the design and implementation of research prototypes and
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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and content analysis methods Applying statistical and computational techniques to analyse large-scale textual datasets Contributing to peer-reviewed journal articles and conference presentations
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of the research project, research may also incorporate advanced optical diagnostics, quantitative image analysis, computational modeling, and remote sensing technologies to improve understanding, evaluation, and