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: Design and implement models for a knowledge graph as part of an R&D team. Research methods and techniques for populating the knowledge graph. Develop models or algorithms to facilitate risk identification
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at the University of Sheffield within the consortium is to lead nationally the development of quantum machine learning (QML) algorithms. The research will involve designing innovative QML approaches and collaborating
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publications is a plus. Experience in designing, developing, and implementing computer vision models and algorithms. Proficiency in Python and its standard coding practices and common libraries. Experience with
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maintenance, production efficiency, and quality control. While the benefits of ML are significant, its adoption also introduces risks such as data privacy concerns, algorithmic bias, model transparency issues
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in simulated environments and with real data on real UAVs. Defining and calculating measures for levels of trust in the developed algorithms is essential. These uncertainty-aware algorithms can self
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machine vision algorithms. The system will be designed with the physical constraints of remote fusion environments in mind, including radiation tolerance, restricted access, and the need for automation and
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Python programming and computational algorithms is highly desirable. Additionally, you will possess a deep understanding of time-series data processing and analysis, as well as experience in human
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, drawing insights and ideas for implementation. Having previous publications is a plus. Experience in designing, developing, and implementing computer vision models and algorithms. Proficiency in Python and
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in our mission to develop clinically useful algorithms, drive high-impact publications, and pave the way for personalised breast cancer treatments. Analyse data from cutting-edge technologies
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in our mission to develop clinically useful algorithms, drive high-impact publications, and pave the way for personalised breast cancer treatments. Analyse data from cutting-edge technologies