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Sessional Lecturer - PPG2012H-S-Topics: Applied AI Systems & Governance: Technology, Policy & Practi
-world policy applications to equip students with the knowledge and tools needed to engage with AI at both strategic and operational levels. Students will learn how modern machine learning models work
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Business Analyst US-NY-New York Job ID: 2026-15444 Type: University Enrollment Management (WS1960) # of Openings: 1 Category: Technology New York University Overview The Business Analyst is responsible
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educational technology, digital tools, and online learning management systems. Strong verbal and written communication skills. Collegiality. Preferred Qualifications: Ability to teach courses aligned with
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contribute to exciting research in learning and control in a collaborative and dynamic environment. About us At the department of Electrical Engineering research and education are performed in the areas
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Broadcast and Studio Engineer - (Broadcast Engineer, Range I) - College of Liberal and Creative Arts
engineering. Environmental/Physical/Special This position serves the BECA Department, the School of Cinema and the Journalism Department. Ability to work at a computer for extended periods. Must work in some
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research groups in computer architecture, databases, machine learning, networking, programming languages & compilers, software engineering, systems, and theory. Faculty from our school are leaders in a
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-driven or machine learning techniques relevant in the context of engineering Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work independently in a
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The Computer Vision-Core Artificial Intelligence Research (Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and Technology (KAUST) is
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technologies, such as low-power long-range (LoRa) and high-throughput, low-latency technologies (5G). In the context of machine learning, communications play a central role in data sharing and in the decision
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Artificial intelligence and machine learning methods for model discovery in the social sciences School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Robin Purshouse