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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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by Industry 5.0 technologies and human–robot collaboration. Key innovations include Digital Product Passports (DPPs) and data-driven tools to improve lifecycle transparency, alongside methods
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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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. Ability to work effectively in a collaborative environment. Strong technical and academic writing skills. willing and able to acquire new skills as needed to support the research projects. Proficient and up
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particular emphasis on applications from psychology, cognitive science and computer science, and the candidate will be encouraged to engage in interdisciplinary collaboration and communication. We are looking
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Max-Planck-Institut für Kohlenforschung, Mülheim an der Ruhr | M lheim an der Ruhr, Nordrhein Westfalen | Germany | 2 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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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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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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or explainable). Professional Experience: Analysis of scientific literature. Contributions to open source projects, machine learning competitions or relevant own projects. Have participated/collaborated in
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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