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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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international training environment. PhD students will work with leading scientists in the field and benefit from their complementary expertise in theory and experiments involving the various messengers
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Description The International Max Planck Research School for Chemistry and Physics of Quantum Materials is a joint PhD program between the Max Planck Institute for Chemical Physics of Solids in
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Description Scientist/PhD-Student position (m/f/div) starting from 1 October 2025 and limited for 36 months in part-time with 67 %. Your key tasks: • Fabrication and characterization of porous
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machine learning approaches. These are similar to earlier work on charge and excitation energy transfer (see https://constructor.university/comp_phys). The project for the PhD fellowship is slightly more
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biogeochemical processes of the coastal ocean and the impact of anthropogenic influences on the natural state. It is located near Hamburg. PhD Position Reference code: 50136009_4 – 2025/KD 2 Commencement date: as
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PhDGermany Find your suitable PhD opportunity in Germany. Find your suitable PhD opportunity in Germany. Back to Overview Working LanguageEnglish LocationGöttingen Application Deadline01 Apr 2026
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PhDGermany Find your suitable PhD opportunity in Germany. Find your suitable PhD opportunity in Germany. Back to Overview Next Working LanguageEnglish LocationGöttingen Application Deadline01 Apr