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railway earthworks. Additionally, the project will integrate environmental data through data fusion and develop automated machine learning tools for anomaly detection and risk assessment. The effectiveness
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learning and AI models to imaging data [3]. The student will be tasked with the development of new models for the early detection of CIN cancers, applying bleeding edge computational methods and machine
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of computer vision and machine learning. Previous experience of real time systems development in Python, OpenCV, PyTorch and deep learning are essential. Experience of C/C++/C#, TensorFlow would be beneficial
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the centre. Successful applicants will have (or be about to obtain) a PhD in computational biology or computer science, with knowledge of multimodal spatial omics data integration and machine learning
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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cutting-edge machine learning methods for spatial omics and for multi-modal data integration. The post-holder will also collaborate on the development of new computational methods to support the CoRE’s
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an expert in the use of advanced statistical inference and machine learning methods for the analysis of omics datasets. You will work with a team of data scientists in Manchester and Oxford to create tissue
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project meetings. You will have a PhD in atmospheric science or climate science, including experience of running and analysing numerical models, and an appreciation of machine learning methods suitable
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operation, aligned with financial and operational incentives. The ideal candidate will have: A PhD in Computer Science or a related field Experience in developing and evaluating reinforcement learning systems
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the Centre’s datasets and using data analytics and machine learning, this Centre strand will build a blended data resource that can help to gauge the prevalence, forms, and distribution of slavery in wars