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Field
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conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both the private and public sectors
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is currently building and commissioning a network of calibrated multi-sensor observatory-class systems, and developing novel machine learning methods with the aim of collecting science-quality data
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experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical and machine learning methods in the context of biological systems Experience with programming (e.g
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and/or gender identity or expression, marital status, military status, national origin, parental status, partnership status, predisposing genetic characteristics, pregnancy, race, religion, reproductive
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Tübingen offers a combination of high-performance medicine and strong research. The goal of the Carl-Zeiss-Project “Certification and Foundations of Safe Machine Learning Systems in Healthcare” is to enable
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Pacific Institute for the Mathematical Sciences | Northern British Columbia Fort Nelson, British Columbia | Canada | about 1 month ago
discretizations and/or machine learning methods. The ideal candidate should have a strong background in numerical analysis, scientific computing, and/or scientific machine learning. We are particularly interested
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
, designing natural language interfaces for more intuitive navigation of the BDC environment, creating BDC-specific foundation models, and enabling large language models (LLM) and machine learning-based
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-dimensional probability, concentration and functional inequalities ? Mathematical aspects of machine learning and deep neural networks ? Free Probability aspects of Quantum Information Theory. While excellent
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position) Designing a machine-learning-based bias correction method using retrospective forecasts and reanalysis data for comparative calibration. Topic 3: Development of seasonal prediction models (one–two
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow