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Field
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of the sensors under various conditions (laboratory and real-world environments), together with the implementation of machine-learning algorithms for data processing, clustering, and quantification of target
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), this role will focus on advanced navigation technologies, built around a unique laboratory specially designed to test incredibly sensitive navigation sensors. The mission of NRDD is to provide expert
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. Key Responsibilities: Develop and implement perception and control algorithms for robotic arms and embodied AI systems. Assist in integrating multimodal AI models (vision, language, force sensors) with
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analysis. They will also work on software that allows the vehicles to navigate and adapt to dynamic underwater environments without human intervention, including algorithms for real-time sensor fusion
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fields (e.g. interpolation, climatological fill, ML-based gap-filling). • Apply noise-removal algorithms (e.g. signal filtering, radar clutter suppression, spike detection) across sensor and remote
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approaches to optimize fundamental mathematical signal models for the unique geometry of OPM sensor arrays. Pioneer the development of 2-MEG research studies which involve two people, both monitored by MEG
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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
computer vision algorithms into the NICE Toolbox. Assist with testing, evaluation, and optimization of implemented computer vision methods. Provide basic user support for researchers using the NICE Toolbox
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processing software applications for sonar and underwater acoustics including algorithm design, implementation, verification, and performance analysis in the Advanced Technology Laboratory (ATL
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Wideband Antenna Arrays is performed. The tasks associated with this project involve, but are not limited to, Sensor node hardware design and antenna evaluation RFSoC-based multichannel acquisition and
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robots and sensor networks increasingly use federated learning (FL) to train shared models across many devices without centralising raw data. In realistic deployments, however, each robot experiences