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Field
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Electronic Engineering, Control Engineering, Computer Science or a very closely related topic: Strong understanding of power electronics principles Excellent knowledge on data-driven machine learning algorithm
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progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The global expected outcomes of the project are to propose a new model for
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computer science, statistics, operations research, or related computational fields. As part of an interdisciplinary research team dedicated to advancing management science, the fellows will develop novel
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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ethical and security standards. Concept and Algorithm Development: Innovate in data science, machine learning, and AI. Data Analysis and Reporting: Contribute to data analysis, reporting, and publication
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Job Reference: 1168098 About the Role: You will play a key role in advancing distributed and adaptive AI methods, focusing on scalable software frameworks, learning algorithms, and orchestration
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questions in quantum information science, and to guide near-term hardware and algorithm co-design. What You Will Do: Specialized research in conception and execution of quantum algorithms on superconducting
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 20 hours ago
Preferred Experience, Skills, Training/Education: 1. Ph.D. in Industrial Engineering, Biomedical Engineering, Data Science, Computer Science, or related fields. 2. The candidate is expected to have a strong