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information-security approvals required by the programme, and engage with internal University stakeholders (IT, HR, Teaching & Learning, faculties) and external partners. We welcome candidates who bring diverse
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Evidence of research achievement at the very highest international level, including a strong record of publications in top-tier venues in AI/machine learning and evidence of significant influence
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reactions. Experience applying Machine Learning to optimise and guide iterative laboratory experiments. Experience of oligonucleotide design and of adapting an amplification method to new target sequences
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feasibility, spatial placement and configuration of NBS and evaluate their performances from a decision support context. The successful applicant must ensure AI and machine learning technologies are well
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comparative analysis across the cities, identifying common lessons learned and policy recommendations contribute to background working papers, policy briefs and blogs, and participate in workshops and webinars
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framework or knowledge graphs; 2) Uncertainty analysis and risk modelling; 3) Port and maritime operations; 4) Cybersecurity or critical infrastructure protection; and 5) Machine learning and real-time data
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of their career. The Career Development Fellowships will enable early career academics to acquire a strong and well-rounded foundation to support future applications for substantive academic roles at Durham
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framework or knowledge graphs; 2) Uncertainty analysis and risk modelling; 3) Port and maritime operations; 4) Cybersecurity or critical infrastructure protection; and 5) Machine learning and real-time data
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wide range of backgrounds and experiences. You should demonstrate: Essential Criteria Relevant academic training and a PhD (or equivalent experience) in a relevant subject such as health economics
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of the Institute, including mentoring and supporting others where appropriate Ensure research is conducted in line with ethical, governance, and regulatory requirements About You You will have: A PhD (or equivalent