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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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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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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