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equitable scholarly environment in research, mentoring, and service. Your work will focus on the SEAMLESS (SEmi-Automated Machine LEarning Search for Semi-resolved galaxies) survey, whose goal is to identify
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demonstrated experience with a set of tools appropriate for working with large-scale data science including application of machine learning. In addition, applicants must have demonstrated leadership experience
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, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression. To learn more about diversity at the U: http
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• Machine learning for wireless communications • Hardware-constrained signal processing for wireless communications • Channel modeling and characterization Applications will be
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Associate selected for this position will work as a member of an interdisciplinary team on research involving the use of statistical and machine learning methods for the development of Immune Digital Twins
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supersonic and hypersonic flows, as demonstrated by application materials. Familiarity with machine learning or data-driven modeling approaches in fluid dynamics, as demonstrated by application materials
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of compressible flow regimes, including supersonic and hypersonic flows, as demonstrated by application materials. Familiarity with machine learning or data-driven modeling approaches in fluid dynamics, as
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, or a related technical field. ● Strong programming skills in Python, Java, etc. ● Expertise in machine learning, neural networks, and deep learning ● Excellent writing and communication skills ● Highly
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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for the next generation of particle physics experiments and also explores other ways AI can accelerate scientific discovery. The group collaborates closely with computer scientists, astrophysicists and