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Starrydata2). The work will include the implementation of machine learning models (neural networks, random forests, SISSO), generative approaches for predicting crystal structures, the use of machine learning
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contribute to the development of fundamental aspects of computer science (models, languages, methodologies, algorithms) and to address conceptual, technological, and societal challenges. The LIG 22 research
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master's degree. For this PhD project, we expect candidates to have demonstrated experience or a strong interest in topics related to uncertainty quantification, probabilistic modelling, and the validation
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University Medical Center of the Johannes Gutenberg University Mainz | Mainz, Rheinland Pfalz | Germany | 9 days ago
The PhD student will: Develop and evaluate statistical and machine learning models for survival analysis Perform methodological comparisons and validation studies Analyze high-dimensional molecular and
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to apply Website https://www.academictransfer.com/en/jobs/357715/phd-high-resolution-modelling-o… Requirements Specific Requirements We are looking for a motivated candidate to develop and apply high
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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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Mobility (IAM), considering ecological, economic, technological, and sociological factors. The RTG's structured PhD program aims to train young researchers in highly automated, networked mobility, featuring
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We offer a fully funded PhD student position within the TruX Research Group headed by Prof. Dr. Tegawendé F. Bissyandé. The position is supported by Google.org and is embedded in a broader research
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more cost-efficient. Together, UESL and IMOS are seeking a motivated and qualified PhD candidate to advance the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By