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Description Are you curious how Deep Learning and Online Learning can be effectively combined to create new learning paradigms? Job description Online learning algorithms achieve robustness often at the expense
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improve overall task efficiency. Topics may include autonomous mission planning, resource-aware scheduling, multi-drone coordination, optimization-based decision-making, and integration of robotic
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, algorithms and programming paradigms to provide input for quantum and HPC system procurements and DOE technology roadmaps. Collaborate with theorists and experimentalists to validate hardware noise models
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Department Harris School Bike Shop About the Department The Bike Shop seeks to solve society’s most pressing challenges by designing and scaling advanced algorithms—particularly AI—that enhance
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probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
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optimization methods for run-time network configuration and control. You will design efficient and lightweight learning-based techniques for automated scheduling, network resource allocation, and parameter
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-RAN architecture Ability to demonstrate fundamental knowledge in the fields of x-haul architecture, radio resource scheduling procedures in x-haul with O-RAN architecture, xApps/dApps/rApps design and
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) Parallelization of scheduling algorithms using tensor processors and SIMD Experimental evaluation in industrial use cases Participation in teaching and administrative tasks (2 SWS) Your profile: Completed master's
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Department: College of Science Classification: 12-month Research Faculty Job Category: Research Faculty Job Type: Full-Time Work Schedule: Full-time (1.0 FTE, 40 hrs/wk) Location: Remote Workplace
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PROTEUS. Ultimately, the causal reasoning and uncertainty algorithms you build will serve as the quantitative engine for the "Copernicus Agent," an AI assistant designed to give European policymakers