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algorithmic agents, the research explores how automation, predictive modeling, and generative intelligence transform sensemaking, decision-making, and adaptive capabilities. The goal is to develop a framework
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concepts in information processing using machine learning algorithms LanguagesROMANIANLevelExcellent Research FieldComputer scienceYears of Research ExperienceNone Additional Information Selection process
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The Marketing group at Rotterdam School of Management, Erasmus University seeks a highly motivated PhD student with strong quantitative skills to study the problem of algorithmic biases in marketing. As machines
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stakeholders in the Dutch battery ecosystem to develop and demonstrate the next-generation algorithms and models for the future Battery Management System. The PhD student will work on topics related to: Develop
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. Demonstrated experience designing analytical frameworks, and experience using machine learning algorithms and Bayesian statistics within the R-language. Demonstrated experience managing project workflows and
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, please visit: https://dakic.univie.ac.at/ . Your future tasks: You will actively participate in research, teaching and administration. This means: You are involved in a well-funded research project
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, tremor-signal acquisition, and algorithm development. 2. Characterisation analysis and interpretation of tremor data, including maintenance of datasets, documentation, drafting technical reports and
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algorithmic consequences. Topics of interest include coarse equivalents of classical graph theorems and parameters, asymptotic minors, and coarse embeddings; Where to apply E-mail job-ref-5sp0nv8qvb
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, University of Warsaw, Banacha 2, 02-097 Warsaw. Employee benefits: access to professional development, social benefits, and training opportunities. More information: https://en.bsp.uw.edu.pl/researchers/ Basic
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, and training opportunities. More information: https://en.bsp.uw.edu.pl/researchers/ Basic responsibilities and obligations The selected candidate will: conduct scientific research in the field