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to fuzzy-based algorithms. You will base your work on two different flood classification approaches, namely a hydrology-based one and a hydrograph-based one and compare these regarding their ability
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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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scheduling to help make offshore wind farms a reality. Job description This post-doctoral position focuses on developing fundamental algorithmic advances for dynamic planning and scheduling in multi-objective
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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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laboratory facilities. More specifically TEC-EF responsibilities encompass at subsystem and instrument level: Payloads with RF interface exploiting different technologies (e.g., analogue, digital, optical
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responsibilities include: Development of a flood classification framework for flood type prediction Comparison of different ML algorithms in a sensitivity study Communication with stakeholders Development of open
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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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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Do you want to develop human-centred RL algorithms to shape
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behavioral component as it would be interesting to study how consumers perceive algorithmic discrimination of different kinds and how companies can mitigate negative perceptions. During their PhD
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deployed and validated to demonstrate LDES market potential. The goal of this postdoc position is to contribute to the modelling, simulation, and optimisation across different LDES technologies, from device