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Field
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associate to work on one or more of the following topics: Mathematical Physics, Spectral Theory, Quantum Chaos, Large Graphs and Quantum Walks. Related areas such as Quantum Information can also be considered
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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relevant expertise: A PhD in Computer Science or a closely related field, with specialization in Quantum computing and Graph theory In this role, you will be responsible for conducting research on graph
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: Statistical analysis & causal inference Data management, collection & visualization Social network analysis & graph theory Online tool/web development Running controlled experiments Game-theoretic modelling
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, REQUIRED EDUCATION LEVEL, PROFESSIONAL SKILLS, OTHER RESEARCH REQUIREMENTS PhD in Mathematics or Computer science, A good understanding of BFT ; Ability to link technical problems and algorithms, graphs
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determined by KUMC LAR policies. Work independently in the design and performance of experiments, develop graphs, document and maintain statistics, and interpretation and dissemination of data. Perform and
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that ingest raw on-chain data (blocks, transactions, smart-contract events) from public blockchains into research-grade databases Developing statistical, graph, and/or machine learning models to study
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Developing statistical, graph, and/or machine learning models to study transaction networks, illicit transaction patterns, and DeFi protocol operations Creating and evaluating tools, documentation, and open
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analysis, combinatorics, computational mathematics, differential equations, dynamical systems, geometry, graph theory, mathematical biology, mathematics education, number theory, probability, and statistics
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following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data management and collection, causal inference, network analysis, graph