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Field
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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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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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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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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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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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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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to the project. Job Requirements: PhD degree in Computer Science, Electrical and Electronic Engineering, or related field. Min 3 years of relevant experience in computer vision, artificial intelligence, etc
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. Coordinate modelling activities across multiple projects and deliver high-quality outputs on time. Integrate new methodologies, including AI and machine-learning approaches, into simulation design. Conduct
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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