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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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processing and machine learning methods, and big data analytics solutions to extract highly accurate large-scale geo-information from big Earth observation data. Our team aims at tackling societal grand
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At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13 TV-L, part
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08.09.2021, Wissenschaftliches Personal The Professorship of Machine Learning at the Department of Electrical and Computer Engineering at TUM has an open position for a doctoral researcher (TV-L E13
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, the details of the process are not yet fully understood. Mechanistic learning, the combination of mathematical mechanistic modelling and machine learning, enables a data-driven investigation of the processes
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These are positions for Doctoral Students, based in Tübingen in an interdisciplinary research group working at the interface of Machine Learning, Medicine, and Biology. Doctoral Students will engage
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Max Planck Institute for Intelligent Systems, Tübingen site, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 7 days ago
, virtual reality, biology and medicine. Using unique 3D & 4D capture facilities, machine learning, computer vision and advanced graphics, we are modeling humans and animals shape and behavior. In the Eye
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning-assisted PSPR optimization of recently developed lean Mg-0.1 Ca alloy
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, storage, accessibility/sharing, archiving, publication, and preparing data for machine learning applications. The Research Training Group RTG 3120 offers, subject to the availability of resources, a
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab