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Engineering, Biomedical Engineering, Medical Imaging, Signal Processing, Applied Physics, Computer Engineering, or a closely related discipline. Strong background in at least one of the following: ultrasound
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also receive training in use of Excel spreadsheets, PowerPoint, Visio, and plotting and statistical analysis using various software platforms. Learning Objectives: Under the guidance of a mentor you will
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-disciplinary areas of artificial intelligence machine learning big data and data analytics software and security mobility and autonomy The Presidential Postdoctoral Fellowship is proudly supported by generous
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and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps
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desirable. Experience with AI, machine learning, optimization, spatial analysis, scenario modeling, or decision-support methods is strongly preferred, especially when applied in transparent, interpretable
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novel findings that inform disease etiology. The candidate should be interested in focusing on multi-omic integration analytics, machine learning, and/or AI. In addition to carrying out research
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. Learning Objectives: By the end of this training/research experience, you will be able to: Explain the structure and functional organization of plant, insect, and/or fungal genomes and describe how genomic
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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of Texas at Austin. In our lab, we are interested in studying how people and machines understand emotions. Recent projects include: how generative AI may be able to provide empathy, how people perceive
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spatial and temporal data analysis using advanced machine learning technologies. The successful candidate will become a part of an interdisciplinary team working to develop machine learning techniques