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oder einen Doktoranden für das neu eingerichtete Forschungsteam im Rahmen des ERC Advanced Grant Projekts „Equilibrium Learning, Uncertainty, and Dynamics“. Schwerpunkte der Forschung liegen auf Deep
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Future. Discover. Together. The Computer Vision & Graphics group of the Vision & Imaging Technologies (VIT) department is looking for a student assistant in the area of deep learning for scene
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strong background in machine learning and deep learning techniques You have familiarity with generative models You also enjoy learning about new topics and contributing your own ideas What we offer Good
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on this critical stage. Specifically, you will research deep learning models for image segmentation to detect damage to concrete buildings. Since conventional models require large amounts of precisely labeled
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In manufacturing, a wide variety of use cases exist where Deep Learning (DL) and Machine Learning (ML) are successfully applied. Examples of use cases include the production of rockets, stem cells
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candidate will show in-depth methodological and applied knowledge in the field of machine learning, especially deep learning, experiences in the area of uncertainty quantification, generative and Bayesian
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will be to use machine learning approaches (deep neural network architectures) to design representations and transferable energy models for proteins. Various resolutions will be investigated in
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Design and use of data spaces and digital twins for materials and autonomous material laboratories Use of deep learning methods to connect theory, simulation, and experiments Integration of high throughput
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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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: Experience with probabilistic graphical models, time series analysis, or deep learning Familiarity with reproducible research practices and open-source collaboration Interest in interdisciplinary applications