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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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resource-efficiency requirements. This collaborative doctoral project brings together the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy
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semiconductor properties to the composition of lead-free double perovskites Your Profile: Master’s degree in theoretical or computational physics, chemistry, materials science, or a similar field Familiarity with
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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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- 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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master’s degree or diploma in physics, applied mathematics, or a relevant engineering discipline Good programming skills and experience with numerical modeling Interest in performing experiments Excellent
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Description In the Leibniz Institute of Plant Biochemistry, the research group Symbiosis Signalling invites applications for a PhD position in biology (m/f/d) (Salary group E13 TV-L, part-time 65
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San Juan http://www.unsj.edu.ar/ Further Information about the Doctoral program in Electrical Engineering https://iee-unsjconicet.org/doctorado_ingenieria_electrica/ Doctoral program in Electronic
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available in the further tabs (e.g. “Application requirements”). Programme Description In line with its statutes, the Studienstiftung des deutschen Volkes (German Academic Scholarship Foundation) supports
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available in the further tabs (e.g. “Application requirements”). Objective The programme aims at fostering strong, internationally oriented higher education systems in Southeast Asia with the capacity