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[#28618, IV-459/24, IV-460/24] Position Title: Position Type: Student programs Position Location: Berlin, Berlin, Germany [map ] Subject Areas: Applied Physics Biomedical Engineering AI/Machine Learning
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genomics, virtual cell models Graph-based neural networks, optimal transport Biomedical imaging, deep learning, virtual reality, AI-driven image analysis Agentic systems, large language models Generative AI
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to the table You study computer science, electrical engineering or a similar subject. Good programming skills in Python Knowledge in deep learning Experience with object detection algorithms, e.g. Yolo or Faster
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning
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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. Furthermore, we
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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. Furthermore, we
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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. Furthermore, we
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you will do Time series forecasting problems with Neural Differential Equations on Graphs Solve PDEs with Physics Informed Neural Networks and train Diffusion Models Develop and test new deep learning
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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collaboration with Q.ANT GmbH in Stuttgart, a deep-tech company that develops photonic computing and photonic sensing products. The goal of this project is the development of highly integrated vapor cells with