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, Artificial Intelligence, Neural Networks, Applications of Statistics in Information Technology. Candidates with a strong research background in Graph Data Mining and Learning are highly desirable
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to make them usable for transparent energy system analyses. You will process and semantically enrich the collected data before it is integrated into a knowledge-graph-based metadata platform. In
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, lost/cost functions, stochastics, computational complexity, logic, graph theory or other areas closely related to it Proven expertise in techniques for descriptive statistics, vectors & matrices and
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performance for up to 3 years. This role involves performing laboratory experiments involving Drosophila, and bed bugs, collecting and analyzing data including preparation of graphs and reports for research
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to work effectively in an interdisciplinary team. PREFERRED QUALIFICATIONS Experience with one or more of the following: knowledge graphs, graph machine learning, link prediction, representation learning
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. Ability to conceptualize and construct tables and graphs. Working knowledge of statistical terminology. Ability to summarize statistical analyses for collaborators. Ability to manage and merge large dataset
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into Excel spreadsheet and generating graphs), and correlate data and recognize discrepancies in results. Also contribute data/findings for use in reports, documents or oral/written presentations. Participate
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this postdoc position, the focus is on methods and algorithms for large-scale graph analytics, in particular network science approaches for analyzing longitudinal, population-scale relational data derived from
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assignments. Skill in creating graphs, charts, and presentations. Expertise with Microsoft Excel and standard office computer applications (spreadsheets, word processors, etc.). Preferred Knowledge, Skills, and
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Postdoctoral Fellow with Professor Morgane Austern. Professor Austern’s group focuses on research in high-dimensional statistics, probability theory, machine learning theory, graph data, Stein method, ergodic