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robust background in quantitative methods, including causal inference and econometric modeling, as well as familiarity with modern computational tools such as machine learning, text analysis, and large
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, optimization, or data analysis Detailed knowledge of relevant models, techniques, and methods used in transportation and logistics, with the ability to develop new ones Interest in curiosity-driven and
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collection and advanced statistical analysis Proven track record of teaching in information systems or related fields Excellent teamwork, leadership, and critical thinking abilities Proven record
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such as large-scale firm-level data, text analysis, or computational approaches is desirable. An interest in topics such as startup dynamics, technology adoption, or the broader implications of digital
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Number Theory, (2) Geometry and Topology, (3) Probability and Stochastic Analysis, (4) Discrete and Geometric Analysis, (5) Statistics and Data Science, and (6) Partial Differential Equations and Modelling
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Number Theory, (2) Geometry and Topology, (3) Probability and Stochastic Analysis, (4) Discrete and Geometric Analysis, (5) Statistics and Data Science, and (6) Partial Differential Equations and Modelling
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Number Theory, (2) Geometry and Topology, (3) Probability and Stochastic Analysis, (4) Discrete and Geometric Analysis, (5) Statistics and Data Science, and (6) Partial Differential Equations and Modelling
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Stochastic Analysis, (4) Discrete and Geometric Analysis, (5) Statistics and Data Science, and (6) Partial Differential Equations and Modelling. The Department of Mathematics (DMATH), headed by Prof. Dr
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research building on the analysis of large-scale blockchain data. This will include, but is not limited to: Designing, implementing, and maintaining scalable pipelines that ingest raw on-chain data (blocks
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semantic can be inferred. At the end of the processing pipeline, a component will aggregate the accumulated knowledge about the discovered attack surface. The ultimate objective is to provide useful and