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Field
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"Practical Quantum Advantage of Reservoir Computing on NISQ Devices." Position Overview The successful candidate will conduct research in quantum and quantum-classical algorithm development, focusing
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the job funded through the EU Research Framework Programme? NextGenerationEU Reference Number 2022.05284.PTDC Is the Job related to staff position within a Research Infrastructure? No Offer Description
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noise, allowing only specific algorithms with relatively shallow quantum circuits to be executed. In the NISQ era, hybrid algorithms run partially on quantum computers and partially on classical computers
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settings. Develop and test algorithms for object detection, tracking, and classification using LiDAR sensors. Help guide and mentor graduate students and other junior team members working on the project
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. Responsibilities* Design, implement, and evaluate LiDAR-based experiments in lab and real-world settings. Develop and test algorithms for object detection, tracking, and classification using LiDAR sensors. Help
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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-photonic computing architectures; Silicon-photonic network architectures Machine Learning Algorithms/Systems: Experience in design and use of ML algorithms; Experience in using ML for designing computing
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to climate, health, and environmental challenges. Strong candidates will have experience in ML algorithms, human-centered ML, or NLP. Supported by an ARC Future Fellowship and CSIRO-NSF project. Computational
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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architectures for explainable dual-process computation Design and development of deep neural network architectures and algorithms for the implementation of dual process computation approaches that improve