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Field
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, computer science, and statistics The objective of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD
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science background, preferably in algorithm design, system programming, networking, operating system, and computer architecture. Experience in low-latency RPC, network stacks, distributed systems, host data plane
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, nonlinear dynamical systems, robotics, and formal methods to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will
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event-based cameras. 2. Developing the first-ever AI/ML algorithm to predict the transition in real time. This will be implemented in benchmark transient multiphase flows, such as bubbly flows, turbulent
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precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related
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on algorithmic, computational, and physical-layer solutions for emerging holographic systems. The results of this project have the potential to shape future consumer, industrial, medical, and scientific
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of Health Informatics as part of a group of over 30 researchers using clinical data to improve our understanding of disease and the effectiveness of treatments, and implementing AI algorithms to deliver safer
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the context of critical illness. This position focuses on computational modeling of host-response mechanisms using high-dimensional multi-omics datasets. The fellow develops novel computational pipelines
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research