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Field
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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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Traditional CFD solvers are too slow for iterative design applications, requiring days to evaluate a single urban design variant. Fraunhofer IBP is pioneering the use of Deep Learning emulators
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 7 hours ago
) in Geometric Deep Learning to join the Machine Learning and Artificial Intelligence (MLI) g roup to perform research in geometric deep learning for materials science. This research is part of
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–2 years, total 3–4 years) on deep learning for medical imaging. This DFG-funded project focuses on developing deep learning methods for medical and scientific imaging. The Professorship for Machine
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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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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working
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them using state-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and
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-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and functional evolution
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., long-read spatial transcriptomics) across diverse organs, from the brain to the gonads, with state-of-the-art computational and deep-learning approaches. Projects may also include detailed molecular and
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foundations of medical deep learning. The project focuses on novel self-supervised objectives, information geometry, mitigating representation bias for rare pathological findings, and building next-generation