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Anomaly detection methods address the need for automatic detection of unusual events with applications in cybersecurity. This project aims to address the efficacy of existing models when applied
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will conduct independent and collaborative research, publish in high-impact journals, and present findings at major international conferences and workshops. You will also mentor and support PhD and
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Automating code generation, SQL query formulation, and data preprocessing pipelines is a crucial step toward intelligent and efficient software development. This project aims to leverage large
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Levin Kuhlmann Research area Machine Learning We are seeking a highly motivated and innovative PhD student interested in exploring the opportunities for using AI to enhance personalisation of services and
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to implement these new methods Lead and contribute to high-quality research publications Engage with interdisciplinary teams and stakeholders Contribute to mentoring, proposal development, and capacity building
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initiatives Key Selection Criteria: PhD in a relevant field with a strong research track record National profile with potential for international leadership Demonstrated success in publishing and attracting
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that unveil the unseen. analyse complex data to uncover insights and pioneer new strategies for molecular sensing and imaging. We’re looking for someone who brings: A PhD in chemical biology, organic chemistry
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statistical software Knowledge of Good Clinical Practice requirements and adherence to research guidelines Experience conducting research involving participants with chronic health conditions If you're ready to
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Neuro-symbolic AI combines the strengths of neural and symbolic methods to efficiently learn and reason over models of the world. Typically, many of the assurances that can be provided by
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through supervision of PhD students, and contribute to the educational and administrative duties of the Department and Faculty such as by taking on course direction roles. As the successful candidate, you