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part of change Driving innovative AI and robotics research Development and implementation, practical application, theoretical analysis and evaluation of AI algorithms Implementation of deep learning and
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The Fraunhofer-Gesellschaft and Hannover Medical School (MHH) are seeking to appoint a suitable candidate at the earliest possible opportunity to fill the joint position (“Jülich model
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safe paths, avoiding difficult terrains such as mud and dense vegetation and preventing collision with obstacles. A key approach in this research field consists of using Deep Neural Networks and
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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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subject. Good programming skills in Python Knowledge in deep learning Experience with object detection algorithms, e.g. Yolo or Faster R-CNN Plus: first experience with 3D object detection Onsite attendance
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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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Engineering, or a related field Advanced knowledge in Machine Learning, particularly in Deep Learning, Large Language Models (LLMs), and Multimodal Machine Learning Relevant references or evidence of previous
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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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Deep Learning The weekly working time is 10-20 hours. The position is initially limited to three months, an extension is intended. We value and promote the diversity of our employees' skills and
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learning architectures for time series with multimodal inputs Analyze and preprocess different data types (text, images, etc.) Review the literature and relevant datasets Orchestrate and document deep