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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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- looking. Sensor technology plays a key role in this transformation, enabling real-time monitoring, automation, and intelligent decision-making. Despite these needs, many water treatment processes still rely
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highly collaborative and interdisciplinary research environment, where you'll work alongside experts from fields such as transport and urban planning, engineering, data science, computer science. Skill
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or directly inside the cell. This approach requires biochemistry or cell biology for the sample preparation and scripting skills for the ET data processing. For more information, please read PMID33762348
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(0)511 762 3623 E-Mail: peth at ifbk.uni-hannover.de Web: https://www.iesw.uni-hannover.de/en/research/main-research-areas/soil-science Legal notice: The information on this website is provided
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(0)511 762 3623 E-Mail: peth at ifbk.uni-hannover.de Web: https://www.iesw.uni-hannover.de/en/research/main-research-areas/soil-science Legal notice: The information on this website is provided
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partnership with the German Federal Waterways Engineering and Research Institute (BAW); building a fully coupled model that simulates surface hydraulics and subsurface flow with relevant turbulence models and
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“DiamondNanoNMR” we are looking for ambitious PhD students (75%, TV-L E13, limited to 3 years). Our mission is to apply quantum information concepts to nanoscale sensing. This emerging technology stands
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solving. Diverse research areas: Work on numerical models, analyzing large data sets, statistical methods and more in a unique scientific environment. Who Should Apply? Emphasizing the physical system
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or replace established methods from computational engineering and computer simulation (such as the finite element method) to represent and exploit relationships along the composition-process-structure-property