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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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learning (FL), where multiple UAVs collaboratively train artificial intelligence models without sharing raw mission data. This supports privacy and efficiency, but it also creates a serious security risk: a
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studying how wireless sensing and AI interact in real systems. The work will be carried out in close collaboration with researchers in wireless communications, sensing, machine learning, and robotics, with
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employees across six locations in the heart of Basel. Our collaborative and international environment fosters innovation, excellence, and impactful research. Join our community and help shape the future
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Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr | M lheim an der Ruhr, Nordrhein Westfalen | Germany | 9 days ago
research stay, industrial internship, or entrepreneurship training. As part of RESOLV, the group has access to state-of-the-art infrastructure and collaborations with partners at RUB and at the Max Planck
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venues including Nature, PNAS, ICML, AAAI, etc. She collaborates widely through the Machine Learning and Global Health network, including with researchers at the University of Oxford, Imperial College
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AITHYRA GmbH - Research Institute for Biomedical Artificial Intelligence of the Austrian Academy of Sciences | Vienna, Virginia | United States | about 9 hours ago
Do you want to help transform human health through machine learning and life science approaches? Join AITHYRA in Vienna for a fully funded PhD at the intersection of machine learning, experimental
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Translational Research in Metabolism. Here, you will join a multidisciplinary research environment where clinical and experimental researchers closely collaborate with colleagues from the Maastricht University
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applications for machine learning. Technology Assessment: Evaluate emerging technologies, instrumentation devices and other tools related to power electronics for electric vehicles applications. Collaboration
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histopathology and clinical information to learn robust cross-modal representations for diagnostic prediction. It pursues two integrated objectives: (i) to develop generative and explainable AI approaches