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always indicate child-rearing periods of the nominee when filling in our nomination form. Mothers can claim two extra years for each child born after their PhD plus any further verifiable parenting periods
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for improved understanding of structural and kinetic processes in electrolytes; and machine learning concepts for improved analysis of experimental and simulated data. Material Synthesis Within this research
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Max Planck Institute for the Study of Societies • | Koln, Nordrhein Westfalen | Germany | about 3 hours ago
. Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme Yes Description/content The International Max Planck Research School on the Social
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or October 2026. Tuition fees per semester in EUR None Combined Master's degree / PhD programme Yes Joint degree / double degree programme No Description/content The Berlin Mathematical School (BMS) is the
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diagnosis, and knowledge of the operation of helicopter systems. • Confident handling of Python and common data science tools. • Knowledge of high-performance computing and machine learning. • Fluency in
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(PhD/CSc.). Please list the date of the first doctorate in the nominee's CV on the application form. How will child-rearing periods be considered in the nomination process? Periods in which the nominee
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of multiple doctorates, the effective date is that of the first doctoral degree (PhD/CSc.). I obtained my doctorate more than six years ago. Can I still apply? We cannot consider your application if you
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models. Your tasks: Research, development, and evaluation of Machine Learning and Deep Learning methods Prototype development Literature review Publication and presentation of scientific results in
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Systems, Tübingen/Stuttgart in Germany. Together we want to build a lighthouse for machine learning and modern artificial intelligence in Europe. The cooperation spans all levels, from leading experts
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private machine learning: Differential privacy (DP) is the gold-standard for privacy protection, but deep learning models trained with DP suffer from privacy-utility trade-offs. You will develop novel model