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Project title: Developing and Evaluating Interpretable Approaches for Human-Centered Machine Learning.Project description: Approaches in interpretable machine learning offer promise in understanding
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tools such as LTSpice (preferred)/TINA/Multisim. Knowledge of low-power biopotential amplifier design and energy harvesting techniques is preferred. Candidates with machine learning skills, particularly
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an important role in the project working collaboratively with the PI and a postdoctoral researcher, and there will be excellent training and travel opportunities. The candidate will also have opportunities
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Invitation to expressions of interest in PhD and Postdoctoral candidates interested in applications in the field Resilience and Risk of Critical Infrastructure. These roles focus on advancing
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postdoctoral fellows in connected schools. As such, interdisciplinary work is welcomed, and candidates from all relevant areas of the Middle East studies are encouraged to apply.
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the School of History, to postgraduates and postdoctoral fellows in connected schools. As such, interdisciplinary work is welcomed, and candidates from all relevant areas of the Middle East studies
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or equivalent) Experience with machine learning tools (e.g., Scikit-learn, Tensorflow, PyTorch, etc.) Track record of publications/and conference papers in software modelling or programming, or system level
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fence-building, policing, military presence, infrastructure-development and domestic policies relating to citizenship and national security. The research will contribute to debates on migration, security
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focus on the Union’s potential to regulate the use of AI and to protect EU digital sovereignty. Existing scholarship on the military applications of artificial intelligence focuses either on global-level
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examining the key personal, educational, professional and systemic influences that define and shape their early careers and practice, including the impact of different learning and professional development