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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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machine learning methods. Provide theoretical predictions to guide experiments, and atomic-scale physical understanding to experimental observations. Publishing findings in peer-reviewed journals
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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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, 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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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
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conducting clinical or preclinical proof-of-concept studies Preferred: Experience in physiological signal processing and the application of machine learning to biomedical data Background in computational
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approach to develop chemical probes, investigate biological mechanisms, and evaluate in vivo efficacy. In particular we use the promiscuous pregnane X receptor (PXR) and constitutive androstane receptor (CAR