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On-device machine learning (ML) is rapidly gaining popularity on mobile devices. Mobile developers can use on-device ML to enable ML features at users’ mobile devices, such as face recognition
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analyses in the neutrino sector with the future DUNE experiment, development of simulations and of reconstruction algorithms and related performance studies , characterization, installation and commissioning
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, research, and develop autonomy algorithms that use sonar data operating in complex environments to avoid obstacles, aid multi-agent decision making and dynamically replan current tasking and sorties. Design
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membrane performance. Therefore, this objective of this research is develop efficient algorithms and models based on deep learning to accelerate the physics simulation for membrane relevant processes, which
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The Centre for Urban Solutions is to provide leadership in developing innovative solutions and sustainable technologies for space creation and urban infrastructure development. The School of Civil
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digitisation. To achieve our goals, we rely on our specific research, development and technology competencies, which are the basis of our commitment to excellence in all areas. With our open culture
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, the project seeks to develop innovative computational techniques and theoretical insights that extend the frontiers of both fields. It focuses on leveraging recent breakthroughs that combine algorithmic
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? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The research activity will focus on the development of algorithms for machine
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Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 2 months ago
of the developed algorithms) Empirical investigation of use cases Scientific exchange and publication of results This position offers the possibility to acquire a PhD degree, which is encouraged. This research
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privacy constraints, robust solutions are essential. This PhD project will develop methods for building reliable medical imaging models that generalize across distribution shifts without retraining