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Field
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. The project will focus on automated distributional shift detection and monitoring, invariant and distributionally robust representation learning algorithms, and deployment-time calibration with uncertainty
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for Quantum-enhanced, distributed radar signal processing techniques that maximise target Signal-to-Noise Ratio (SNR). The algorithms we will derive will be applied and tested in the context of target detection
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Faculty Professor Assistant - Full-Time School of Computing and Information-RC - Pennsylvania-Pittsburgh - (25006623) Tenure-Stream Assistant Professor for Quantum Algorithms As the University
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, financial networks, e-democracy, voting, social networks, online analysis with delay, and theory of distributed algorithms. In our group, we work on both theory and practice: some members of our group focus
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classification algorithms need to be extended with the capability to detect out-of-distribution environments and to autonomously infer their traversability. In this thesis, you will design a semantic
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causal inference in heterogeneous data environments, addressing the challenge of enabling trustworthy causal analysis across distributed datasets while preserving privacy. The successful candidate will be
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-readable representations, such as distributed representations of text augmented with random noises [1] or unnatural text curated by replacing sensitive tokens with random non-sensitive ones [2]. First, such
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will focus on models and algorithms for the intelligent control of bidirectional energy flows, the integration of vehicles into microgrids and smart grids, and the provision of ancillary services such as
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edge AI hardware/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a
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action discretization, exploration parameters, and decision intervals significantly affects algorithmic convergence and system performance. Distributional RL techniques, such as quantile regression and