151 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" positions at Forschungszentrum Jülich
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a team
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, machine learning, energy technology or related subjects Prior experience in building predictive models using regression techniques, neural networks (CNN, GNN) or symbolic regression Experience in
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. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning. Your tasks: Development and comparison of data driven models for the prediction of stresses in
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange
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this limitation in the use of satellite observations by make a direct use of radiance observations retrieved by satellites using machine learning without the need of radiative transfer calculations. The new model
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, and training methods - across multiple technological platforms - photonics, electronics, biological neurons. Responsibilities and tasks This PhD project aims to develop, verify, and benchmark learning
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Your Job: Modeling and characterization at molecular level of selected biological processes by performing classical molecular dynamics, and employing enhanced sampling methods and machine learning
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team across institutes Very good communication and organizational skills Very good command of the English language (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements
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quantify model uncertainties For further information visit our website http://www.fz-juelich.de/ibg/ibg-3/EN/Home/home_node.html or contact us via the contact form. Your Profile: Master’s degree in
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Your Job: Energy systems engineering heavily relies on efficient numerical algorithms. In this HDS-LEE project, we will use machine learning (ML) along with data from previously solved problem