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professional learning programs in generative artificial intelligence for academic staff to enhance the learning experience and digital productivity; Demonstrated record of success in developing and implementing
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proficiency using Microsoft Outlook, Word, and Excel. Heavy rigid license and blue card desirable but not essential. Availability to work on weekends. Ideally, you will also bring: (1) certificate of competency
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artificial intelligence and machine learning applied to genomics and biological systems. The ideal candidate will have interdisciplinary expertise spanning computer science, mathematics, and genomics, enabling
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principles and practices. You will be confident using educational technologies including learning management systems, content creation tools, learning analytics, and emerging tools such as artificial
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artificial intelligence. With excellent interpersonal skills and a collaborative approach, you will also show a clear commitment to equity and inclusion in both your learning designs and your interactions with
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integration patterns, data validation frameworks, and data quality management Experience working with Artificial Intelligence/Machine Learning data requirements and establishing data pipelines for automated
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Science & Artificial Intelligence (DSAI) Interaction, Technology & Information (ITI) Our PhD students and faculty conduct world leading research in many areas of Computing and Computing applications
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Research Areas and Capabilities: Digital Engineering, Artificial Intelligence (AI) and Human Machine-Teaming Aerostructures and Flight Science Advanced Composite and Alloy Additive Manufacturing Propulsion
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Bednarz. "Explainable ai and reinforcement learning—a systematic review of current approaches and trends." Frontiers in artificial intelligence 4 (2021): 550030.
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. 23rd Australian Joint Conference on Artificial Intelligence (AI'2010) [Springer Lecture Notes in Artificial Intelligence (LNAI), vol. 6464], Adelaide, Australia, 7-10 December 2010, Springer, pp82-91 R T