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. For this PhD position you will work on modelling the processes and feedbacks that couple the AMOC and polar ice sheets, with particular focus on sea ice and (North Atlantic) deep-water formation regions such as
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GIS and spatial analysis Data science, data mining, and information retrieval (multi-modal) Machine learning, deep learning and artificial intelligence (Initial) Experience in grant acquisition and
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models (e.g. mixed-effects regressions, Bayesian analyses). You preferably have experience supervising and/or teaching students. You preferably have knowledge of swarm robotics and/or deep learning
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: Automated tracking of ankle muscle fascicle kinematics in both superficial and deep muscles will allow for the intuitive and coordinated control of powered prostheses following leg amputations. In
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anthropology, sociology). You are eager to engage with fundamental questions about language and technology. You bring solid technical skills and deep, critical understanding that you wield for thoughtful
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models. Requirements The successful applicants will have: A solid computational background, an interest in cognitive neuroscience a and strong deep learning programming skills. Ability to work in an
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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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technologies, and performances; LEO-PNT systems, signals, receivers and performance; Deep space navigation systems, signals and receivers for Moon/Mars/Solar System; EGNOS system, receivers and techniques
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(PDT) subsystems including deep space and near-Earth transponders, proximity and intersatellite (ISL) link equipment, high-speed downlink modulators and digital communication equipment. Hereinafter
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(PDT) subsystems including deep space and near-Earth transponders, proximity and intersatellite (ISL) link equipment, high-speed downlink modulators and digital communication equipment; radio frequency