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- MOHAMED BIN ZAYED UNIVERSITY OF ARTIFICIAL INTELLIGENCE
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- University of North Carolina at Chapel Hill
- Mohamed bin Zayed University of Artificial Intelligence
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
related quantities obtained from non-local matrix elements, as well as on the development of algorithms and software for large-scale lattice QCD simulations on DOE leadership-class computing facilities
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-driven research group working at the intersection of computational genomics, clinical artificial intelligence, and imaging genetics. This position offers an exciting opportunity to develop novel algorithms
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. · Developing and implementing computationally intensive algorithms using high-performance computing (HPC) clusters. · Managing and analyzing multiple large-scale datasets, including UK Biobank (UKBB
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algorithms (e.g., NPE) for strong gravitational lensing parameter estimation. Implement domain adaptation techniques to improve model robustness and transferability between simulated and real survey data
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-omic molecular profiling, including genetics, transcriptomics, microbiomics, metabolomics, and immune system markers. The division leverages this data and advanced AI, medical, and computational models
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, characterization, optimization, and autonomous decision-making. Advance Bayesian optimization, active learning, machine learning, scientific models, genetic algorithms, and AI agents in physical laboratory systems
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, and multi-omic molecular profiling, including genetics, transcriptomics, microbiomics, metabolomics, and immune system markers. The division leverages this data and advanced AI, medical, and
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: upper-division labs, quantum mechanics, quantum information, quantum algorithms, statistical mechanics, and mathematical methods in physics. Key Responsibilities The successful candidate will teach
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and efficiency of life sciences research. Developing the algorithms, infrastructure, and governance necessary for such analysis can simultaneously enhance hypothesis generation, computational modeling
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. Developing the algorithms, infrastructure, and governance necessary for such analysis can simultaneously enhance hypothesis generation, computational modeling, and post hoc support for laboratory studies and