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- European Space Agency
- Delft University of Technology (TU Delft)
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- Eindhoven University of Technology (TU/e); Published yesterday
- Eindhoven University of Technology (TU/e); yesterday published
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to source localization based on microphone arrays or distributed sensors. This PhD project will focus on the development of novel methods and algorithms for airborne noise source localization in generic urban
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intelligence (AI) and machine learning(ML). Duties This position combines knowledge of the Earth observation (EO) domain (EO instruments, EO data, EO algorithms, modelling, etc.) and AI/ML, as well as data
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of privacy-preserving artificial intelligence for the benefit of humanity. What You Will Do: Research (Federated Continual Learning): You will develop novel and privacy-preserving algorithms that allow
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will develop novel and privacy-preserving algorithms that allow distributed devices (smartphones, wearables) to learn from new data streams over time (Continual Learning) while collaborating globally
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participation in several European and Dutch national projects. Our key research areas include ultra-reliable low latency communications, resource allocation, digital twins, distributed massive MIMO and flexible
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strategy; participate in internal research sprints to explore, test and validate novel EO concepts, algorithms and workflows in a fast-paced, collaborative environment; engage with the innovation ecosystem
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collaborate with software engineers, systems engineers, and operations specialists, and you will see your algorithms tested on realistic scenarios and data. Candidates interested are encouraged to visit the ESA
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focus often neglects other dimensions of processes, such as the time between activities and the time required for specific steps, the spatial distribution of tasks across locations, or the intricate
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classical computing algorithms are NP-hard or, in general, difficult to implement. Within your application, please provide a research proposal (no more than five pages) answering the following questions: What
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-board Payload Signal and Data Processing algorithms and techniques for RF payloads and instruments in close collaboration with TEC-ED; and Time and frequency references, modelling, design tools