52 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions at Forschungszentrum Jülich in Germany
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the use of large language models to support neural network design and data preprocessing. The position involves close collaboration with experts in cardiovascular simulation and Scientific Machine Learning
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Your Job: In this position, you will be an active member of the SDL “Fluids & Solids Engineering” and will collaborate strongly with the SDL “Applied Machine Learning”. You will have the following
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the following areas desirable but not essential: electrocatalysis, rheology, coating technology, machine learning Intrinsic motivation to show initiative, creativity, and to work independently Excellent
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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datasets with machine learning methods, and software development are beneficial Good organisational skills and ability to work systematically, independently and collaboratively Effective communication skills
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grids and its components Excellent knowledge and experience in programming Python Excellent knowledge and experience in machine learning Experience with git is welcome Excellent ability for cooperative
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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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, engineering, physics, biophysics, applied mathematics, computational biology or a related quantitative field Strong background in deep learning for image analysis / computer vision, ideally on microscopy time
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machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In
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Your Job: We are looking for a PhD student to contribute to the development of fast, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular