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Field
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relevant, yet complex and challenging to resolve spatially, provide an ideal benchmark for the method’s performance. Microorganisms such as bacteria, furthermore, play important roles in all types of niches
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. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing
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. Proficiency in Python programming and major ML/DL frameworks (e.g., PyTorch, TensorFlow). Solid understanding of optimization and regularization methods for training complex neural networks. Practical knowledge
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the start of the appointment. * Applicant must have demonstrated research experience in Quantum Information Science, Quantum Computing, and/or Quantum Networks. Preferred Qualifications: * Experience in
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Job description Climate extremes continue to intensify across Europe, creating complex environmental management challenges for river systems. Traditional infrastructure designed for single hazard
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 16 hours ago
inequities, and other complex social problems. His research focuses on youth experiencing homelessness, where he has transformed the field’s understanding of how social networks shape health and well-being
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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). The team focuses on the communication networks between cancer cells and stromal components, aiming to uncover how these interactions contribute to therapy resistance, metabolic adaptation, and cancer cell
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University of Southern California (USC) | Los Angeles, California | United States | about 2 months ago
). Solid understanding of optimization and regularization methods for training complex neural networks. Practical knowledge of interpretability methods to ensure ML outputs are meaningful in scientific