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modelling knowledge, incorporate reliability/uncertainty, and/or explainable models. The position is in the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department
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modelling knowledge, incorporate reliability/uncertainty, and/or explainable models. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/293458/phd-research-fellow-in-deep
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related field from a recognised university. Knowledge and experience in modelling and simulation of microgrids or power systems using ETAP and/or MATLAB/Simulink. Experience in distributed energy resources
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evaluation Knowledge of numerical simulation, for example with land surface or hydrological models Genuine interest in data science and earth sciences Good organizational skills and ability to work both
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student in areas related to Computer Science. Basic experience in health data analysis. Python programming. Knowledge of machine models and deep learning. Intermediate English level. Specific Requirements
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to push the frontiers of multiphase reactor modeling and accelerate the scale-up of emerging net-zero technologies? Join us at the Department of Chemistry and Chemical Engineering ! About us Our
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skills: Good knowledge of ML/AI based techniques to develop fast surrogates (deep neural networks) and capability to develop own efficient model learning schemes (deep learning techniques, representation
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related to staff position within a Research Infrastructure? No Offer Description Understanding and modeling material behaviour is central to many engineering challenges. This fully funded Doctoral student
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 5 hours ago
methods, ideally with deep-learning models and transformer architectures Basic knowledge in German (approximately equivalent to CEFR level A1 / A2) Very good English knowledge (approximately equivalent to
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | about 5 hours ago
learning model on observations and physical models to represent cloud processes across scales from individual turbulent eddies to cloud systems spanning hundreds of kilometers. We will use this fully data