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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models
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of interest include, but are not limited to, stochastic, discrete, large-scale, and data-driven optimization, machine learning methods for sequential decision making, or stochastic modeling and prescriptive
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support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
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project “Analytics for Learning with Machines” (ALMA) The position is TV-L E13, 75%, limited to 3 years, funded by the Deutsche Forschungsgemeinschaft (DFG). The project is a Franco-German collaboration
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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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Signal Processing and Image Analysis group (DSB), Section for Machine Learning, at IFI. DSB has seven full-time and five adjunct positions and carries out research across image analysis and machine
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) Cleaning and managing large datasets from administrative data sources or online learning platforms Causal machine learning (e.g., double/debiased machine learning (DML), causal forests, generic ML) Learning
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profile and an interest in developing new AI models for high-dimensional biological data. You should have a solid foundation in areas such as machine learning, applied mathematics, statistics
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statistical modeling, machine learning, data analysis, and reporting Proficiency in Python or R Ability to plan, execute and control a project, establishing realistic estimates and reporting timelines Advanced