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, with a particular focus on Large Language Models (LLMs) and LLM-based systems. Furthermore, there will be the possibiltiy to gain experience as a co-supervisor for the PhD students associated with
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statistical and machine learning methodologies to analyze and predict aspects of the collected data With the guidance of Drs. Stuber and Bruchas, develop experimental methodologies related to two-photon imaging
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writing course) 324: Social Network Analysis 327: Concepts of Machine Learning 357: Intro to Data Storytelling 426: Museum Informatics 496: Computer Networks 505: Information, Organization, and Access 510
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programming, GeoAI, deep learning foundation models, drone and field work experience. Demonstrated high level of achievement in related research productivity and academic writing. Technical skills in computer
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of students and can include, technique development, microscopy-spectroscopy, analysis/programming (including AI and machine learning) and materials-focused studies. We use innovative high-resolutionidentical
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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Responsibilities Conduct foundational research in adversarial machine learning, exploring novel attack vectors and defense mechanisms for AI agents and large language models. Develop formal verification methods and
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dynamical systems), epidemiological modelling, data analysis (statistics, machine learning). • in scientific programming (preferably Python, Matlab, R) Genuine interest in the analysis and modeling
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483–485. Barocas, S., Hardt, M., & Narayanan, A. (2021). Fairness in Machine Learning. Retrieved from https