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Field
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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for automatic process generation. Generative approaches, using deep learning algorithms, can generate new process structures, surpassing conventional optimization techniques. Objectives of the ATHENA project
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transparent and intelligible. Although explainable AI methods can shed some light on the inner workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and
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for multimodal machine learning, combining large-scale image data with molecular profiling and clinical data. This includes, for instance, research on deep learning-based image analysis and data assimilation
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/10.1101/2022.11.14.516440 [3] Triage-driven diagnosis for early detection of esophageal cancer using deep learning http://doi.org/10.1101/2020.07.16.20154732 Preferred skills/knowledge We are seeking a
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the optimisation strategies to enhance the performance of complex machine learning models such as deep learning model and large language model. Applicants need to have strong background and track records of research
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of black-box machine learning models such as deep neural networks, they have severe drawbacks and limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable
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. Cranfield University is a world-leading postgraduate institution renowned for its applied research and deep industry connections, particularly in aerospace, defence, and security. Its Centre for Electronic
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workings of black-box machine learning models such as deep neural networks, they have severe drawbacks and limitations. The field of interpretable machine learning aims to fill this gap by developing
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platform. Initially, a black box deep learning approach will be implemented. However, due to the need for robustness, transparency, and explainability (e.g. for quality control across sectors), the research