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ability to conduct archival research on English materials ranging from 1300-1950; Experience working with large quantities of data and/or with databases; Excellent organisational skills; The ability to work
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understanding of the past. This doctoral project aims to integrate diverse historical resources—primarily maps and texts—to enhance spatio-temporal information retrieval using GeoAI and Large Language Models
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of large data analyses as well as very good English language skills are mandatory. Knowledge of the German language and experiences with stakeholder interactions are a plus. Workplace Workplace We offer Your
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candidate with a strong background geology/geomorphology, or a related discipline, a strong interest for evolutionary biology, and who is interested in bridging field data, computational modeling, and large
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for decentralized and distributed data-driven methods for Federated Learning on resource-constrained networks. Your research within the project will contribute towards your doctoral degree at ETH Zurich. You will be
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university near Zurich. You have an interest in law or policy diffusion, privacy law, and international comparative law. Knowledge of privacy / data protection law is highly desirable. You have excellent
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. You feel confident to work with big data and present the findings in a clear and concise way. You hold a Master's degree, preferably in Finance, Economics, Econometrics, or related fields, and are
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, data science, and industrial applications. As part of this position, you will: Develop and implement advanced Machine Learning models to analyze large datasets, including image and time-series data
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imaging, single cell tracking and quantification, large volume 3D bone marrrow imaging with single molecule sensitivity, and ai-supported computational analysis. Job description We are seeking a highly
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recognises that blockchain systems' large-scale characteristics emerge from the continuous interactions between diverse agents operating under specific protocols and rules. We employ sophisticated data