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towards better understanding what replication can mean for qualitative research. Last but most topically, the theme of replication raises a host of questions in relation to machine learning and artificial
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the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use similar techniques to make a statistical inference of the population of subhaloes by
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of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical
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reference monitor on top of seL4 for the secure containment of AI agents. We will develop new mechanisms for dynamically controlling agents’ capabilities and information flows, together with machine-checked
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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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computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in Python and deep learning frameworks (e.g., PyTorch) Interest in applied, industry
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to teach foundational law modules being desirable. Alongside teaching, the role provides protected time to develop an independent research agenda, supported by Durham University's vibrant research
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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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, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities
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the assembly, management, and analysis of complex datasets, including the application of machine learning techniques to develop a range of models such as predictive and image-recognition models. A key aspect of