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Your Job: Developing and implementing QC algorithms (QAA, QAOA, QSVM), quantum AI algorithms, use case adapted algorithms to test and benchmark latest technology focusing on gate-based QC Advancing
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multi-agent autonomous systems and related technologies. This will include development of distributed monitoring algorithms enabling agents in a multi-agent swarm to autonomously locate other agents in
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, estimation, and identification algorithms that directly interface with physical hardware. We work closely with industry partners. Our research has led to several methods now used in commercial products. We
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design, development, and validation of material, control systems, and algorithms for next-generation soft haptic actuators and experiences. Note that the research involves significant interactions with
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includes signal processing with emphasis on development and optimization of algorithms for processing single and multi-dimensional signals that are closely related to applications and applied research
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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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small highly motivated inter-disciplinary team working towards a shared goal. You will be responsible for the design and testing of original machine-learning based algorithms and models for multi-modal
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beginning May 2025 to conduct research under the supervision of Prof. Nick Laneman and collaborating with other leading faculty in the ND Wireless Institute and SpectrumX, the NSF Spectrum Innovation Center
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algorithms for the scanner using machine learning and deep learning. Qualifications: The position requires some background in machine learning, optimization, and deep learning. Some familiarity with MR physics
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. JOB DESCRIPTION AND POSITION REQUIREMENTS: Prof. Bill Bahnfleth and Assoc. Prof. Greg Pavlak are seeking a self-motivated, highly skilled Postdoctoral Scholar to work on funded research in the area of