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boson decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis
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and retirement programs. To learn more about USC benefits, access the "Working at USC" section on the Applicant Portal at https://uscjobs.sc.edu. Research Grant or Time-limited positions may be eligible
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theory, physics-based simulation, and machine learning. Job description The PhD project will develop machine-learning methods for atomistic materials modeling Possible research directions include machine
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systems, from data ingestion to model deployment. Hands-on experience with machine learning frameworks, particularly PyTorch, including model training, fine-tuning, evaluation, and experimentation
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methodological developments along the chosen direction (inference, active-matter theory, or machine learning). ◦ Algorithmic implementation and validation of the developed tools. 5. Validation on model systems
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values across different omics layers and platforms. Cross-omics data fusion and representation learning for comprehensive systems biology modeling. Identification of causal relationships and biomarker
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experimental model systems. Under the direction of the Principal Investigator, the Research Teaching Specialist V will perform cellular and molecular biology experiments related to the ongoing investigatory
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of computational and applied mathematics, including but not limited to data-driven numerical modeling, scientific machine learning and AI for science and engineering, computational uncertainty quantification
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, selectivity and phenotype mechanisms directly in primary human cells and employ machine learning methods for data analysis. This includes activity-based profiling, global- and phosphoproteomics and phenotype
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, competitive degree offerings and stellar faculty. For more than 140 years, the University of Arkansas at Pine Bluff has worked to create an environment that inculcates learning, growth and productivity while