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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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experience in the analysis of metagenomics and/or biological high-throughput data Knowledge of statistical and machine learning methods in the context of biological systems Experience with programming (e.g
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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Europe. In the Monitoring & AI department, you will be involved in the development and implementation of AI and machine learning (ML) tools for monitoring and operation of CO2 storage sites. Key
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for data-efficient exploration and optimization within the process parameter space as well as for adaptive, data-driven machine learning to map the electrolysis process to a digital twin. Data workflows and
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resource efficiency. A physics-based model for monitoring the condition of helicopter components is being developed as part of this project. With the help of flight test data, this model is to be calibrated
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take place monthly. A lecture series on theoretical and experimental neuroscience as well as machine learning is addressed primarily to doctoral students. Lectures are held by principal investigators
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conducting a literature review on state-of-the-art methods for condition monitoring of blade bearings in wind turbines. Furthermore, you will focus on identifying additional suitable AI approaches for blade