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Field
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to possess robust empirical research knowledge within the realm of political science and be proficient in conducting quantitative analyses. Experience with large language models, machine learning, and/or
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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necessary lead relevant meetings. To undertake any other duties relevant to the programme of research. Job Requirements: PhD degree in Computer Engineering, Computer Science, Electronics Engineering or
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of Medicine We focus broadly on quantitative and machine learning techniques in multiple modalities of medical imaging (e.g. fundoscopy images, OCT scans, MRI, CT, X-ray and digital pathology). We bridge
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are reshaping how we learn, work and participate in democracy, our centre tackles the promise and peril of hybrid intelligence—human and machine working and learning together. AI LEARN’s mission is to establish
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verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication
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compression testing UV–Vis spectroscopy Surface profilometry Optical microscopy Scanning electron microscopy (SEM) Experience using or working with machine learning / AI approaches for materials development
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on precision viticulture and related topics Analyse complex datasets using statistical and machine learning methods Publish in reputable national and international journals Collaborate with interdisciplinary
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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: • Graph-based learning and community detection: Identify cohesive and antagonistic groups within signed networks. • Machine learning and network embeddings: Measure consensus, polarity, and opinion shifts