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, design and implementation of an immersive training tool that leverage spatial computing, computer vision, and on-device AI to support automated feedback, skill evaluation, and scalable surgical and trauma
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convection. The objective is to develop reduced but realistic models of cumulus life cycles that may be applied toward cumulus parameterization and/or machine-learning algorithms for predicting short-term
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science, machine learning or a related field. Strong experience working with clinical, biomedical, genetic, electronic health record, or health administrative data. Experience with large language models
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sciences.Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Proposals that include deep or machine learning
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monitoring. Familiarity with computational image analysis, scripting (Python, MATLAB), or machine learning–based image workflows. Experience with method development, imaging assay optimization, or pipeline
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, the Theory of Superconducting Quantum Devices group specializes in quantum computing and quantum optics in circuit QED, a leading quantum computer architecture. Areas of interest include but are not limited