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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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. The scholarship holder will normally be expected to reside in the Munster region and may have opportunities to teach or tutor while undertaking their PhD (which would be remunerated separately). It is not possible