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Field
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role involves designing and integrating real-time software algorithms with robotic hardware, including perception, control, communication, and safety modules to enable safe, precise, and reliable remote
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the EU Research Framework Programme? Horizon Europe - ERC Reference Number 101292603 Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD in Biomedical
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experimental settings. In addition to fieldwork, the PhD candidate will contribute to the development of novel inversion algorithms for EMI and GPR based on full-waveform inversion techniques. These methods aim
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2026 - 23:00 (Europe/Oslo) Country Norway Type of Contract Temporary Job Status Full-time Hours Per Week 37.5 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme
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21 Mar 2026 Job Information Organisation/Company Tallinn University of Technology Research Field Computer science » Other Engineering » Aerospace engineering Engineering » Materials engineering
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Engineering » Computer engineering Engineering » Electrical engineering Engineering » Knowledge engineering Researcher Profile First Stage Researcher (R1) Application Deadline 29 May 2026 - 23:59 (UTC) Country
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algorithms that combine Reinforcement Learning techniques like Partially Observable Markov Decision Processes (POMDPs) with cognitive inference modules capable of modelling human beliefs, intentions, and goals
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | 17 days ago
fundamental research across computational, physical, and social dimensions, we shape artificial intelligence and robotics to advance knowledge and benefit society. We are offering a PhD Position (f/m/d
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field” imaging techniques to solve many important problems in biology and change clinical practice in respiratory medicine. Our ongoing research program involves developing new imaging technologies
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this project, you will develop the next generation of federated machine unlearning algorithms—methods that can efficiently deliver genuine, verifiable, and robust erasure without sacrificing model performance