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independent and collaborative research in electromagnetic sensor systems, including theoretical analysis, numerical modeling, AI-assisted sensing algorithm development, and experimental validation
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independent and collaborative research in electromagnetic sensor systems, including theoretical analysis, numerical modeling, AI-assisted sensing algorithm development, and experimental validation
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independent and collaborative research in electromagnetic sensor systems, including theoretical analysis, numerical modeling, AI-assisted sensing algorithm development, and experimental validation
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evaluating their performance relative to classical machine learning and statistical methods. The research would combine algorithm development with implementation and evaluation on current quantum computers
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design, prototyping, algorithm development and testing, manuscript preparation, mentoring, and lab management. The selected candidate may engage in other activities such as grant proposal development
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datasets. Strong foundation in signal processing, statistical analysis, computational modeling, or algorithm development. Ability to process, clean, analyze, and interpret complex datasets
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learning, applied mathematics, computer science, or a related field; Expertise in computational methods, data analysis, software and algorithm development, modeling, machine learning, high-performance
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condensed matter physics, chemistry, applied mathematics, computer science, or a related field; Expertise in computational methods, data analysis, software and algorithm development, modeling, machine
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Natural Science and the College of Engineering. CMSE centers around computational approaches, encompassing algorithm development, high-performance computing, scientific modeling, and data science
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across different genomic regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and