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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 16 hours ago
.) Departmental Preferred Experience, Skills, Training/Education: Ph.D. in design-related discipline, specializing in deep learning and biometrics. A strong background with evidence of experimental and AI/machine
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Information Engineering and Media to develop Machine Learning and AI algorithms for real-world and media-related applications. The Research Associate/Research Fellow is also expected to support teaching
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are essential. Preferred Qualifications: Prior experience with machine learning (ML) in high-energy physics is highly desirable, though not required. Appointment Details: The position is expected to be based
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ethical and security standards. Concept and Algorithm Development: Innovate in data science, machine learning, and AI. Data Analysis and Reporting: Contribute to data analysis, reporting, and publication
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Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related disciplines. Knowledge of autonomous vehicles or cyber security will be
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to develop and optimize scalable experimental protocols across diverse material families. This role is part of a multidisciplinary team integrating materials chemistry, machine learning, and autonomous
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have a background in qualitative research and community engagement. An interest in patient engagement and participatory action is a benefit, and either experience in, or interest in learning mixed
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antigens, T cell receptor (TCR) and antigen interactions and their crucial role in anti-cancer immune responses. You'll leverage your strong background in computational biology, machine learning, and
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machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role. This post would be ideal for an ambitious and
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integration Key areas: § Metagenomic and 16S rRNA sequencing analysis § Single-cell RNA-seq and proteomic/metabolomic data integration § Machine learning and AI applications in microbiome data