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Field
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collaborations and perform cross-species comparisons. We use machine learning techniques for neural data analysis and computational modelling with a special interest in biologically-inspired deep learning and AI
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training programme at the start of the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work
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through multi-modal sensing, autonomous UAV platforms, and advanced AI-based analysis. A central focus is the detection of defects in bridges and viaducts using X-ray, LiDAR, visual and acoustic data
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to collaborative project across teams within our Center, as well as joined initiatives with external academic and industrial partners Data analysis, interpretation, visualization and integration Keeping an industry
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should have documented background in the following areas: Electrodynamics Data analysis for scientific applications Programming (e.g., Matlab, Python, C, C++) for scientific applications Previous
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should have documented background in the following areas: Electrodynamics Data analysis for scientific applications Programming (e.g., Matlab, Python, C, C++) for scientific applications Previous
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to games, it speaks to larger challenges in the museum sector. The programme of research is expected to encompass analysis of primary and secondary sources, qualitative research involving developers and
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research. The cluster currently has 10 faculty members, whose research interests include computational geometry and topology, graph and FPT algorithms, algorithms for massive data, geo-visualization, and
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to capture the level of attention, emotional arousal, and visual fixation during decision-making. At the same time, behavioral data such as product selection, reaction time, price sensitivity, and
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change in crisis responses over the long term. You will combine historical source research with digital methods to analyse large-scale corpora of textual and/or visual sources. You will work with existing