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: https://www.hds-lee.de/admission/openpositions/ Your Profile Mandatory qualifications are: M. Sc. degree in meteorology, physics, mathematics, or a related field Good knowledge in data handling and
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, robustness, and fairness of LLM-based research indicators. The main use case for assessing the quality of publications is to improve the reliability of literature-derived data in our technology database
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drought Motivation for modelling and data analysis Programming experience or willingness to develop it Good English communication skills (at least B2 level according to the CEFR: https://go.fzj.de
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prediction. In this thesis, you will focus on the quantification of model uncertainty, that arises from limited knowledge and the quantification of data uncertainty, that arises from data-inherent
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embedded FPGA-based intelligence near seismic sensor arrays, you will explore methods to maximize prediction performance while minimizing data transmission and latency. Your key tasks include: Evaluating
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must be inferred from models that are incomplete and data that involve errors. For such challenges, Bayesian analysis using Markov Chain Monte Carlo (MCMC) has become the gold standard. For addressing
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and interpretation of experimental data Your Profile You are studying physics, material sciences, engineering, or a related subject at a university and are looking for a topic for a master's thesis
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-based characterization techniques, requiring visualizing, analysis and interpretation of experimental data. The research results shall be published in peer-reviewed scientific journals and presented at
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well as to allow video information to be shared for both marketing, analytics and editorial purposes. By accepting optional cookies, you consent to the processing of your personal data - including
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designing and evaluating machine learning models for benchmark grids and, depending on data readiness and project progress, by testing them on a real campus network with high-quality consumption and