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Responsibilities: Conduct research on the design and analysis of scalable machine learning systems using convex/nonconvex optimization and federated learning methods. Develop algorithms and prototypes
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for three consecutive periods (2014-2018 and 2018-2022 and 2023-2026). ICN2 comprises 19 Research Groups, 7 Technical Development and Support Units and Facilities, and 2 Research Platforms, covering different
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active research interests in quantum computing, quantum algorithm/software development and applications in decarbonisation who can take full advantage of the unique opportunities the QDA provides
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | 2 months ago
. Duties/Responsibilities Analysis (50%) Develop machine learning algorithms to analyze ground magnetic field perturbations Analyze the results using machine learning interpretability techniques
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computational algorithms of different datasets, and implement novel algorithms within the framework of existing code, providing documentation and user support. You will be responsible for leading statistical
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* Conduct a literature review on algorithmic bias and its impact on marginalized communities. Create an annotated bibliography and develop a structured research paper outline. Draft key sections
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intelligent behavior it generates, including perception, motor planning, learning, reasoning, and problem-solving. This ambitious initiative promises to offer unprecedented insights into the brain's algorithms
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computational physics, machine learning, or related areas. Practical experience in developing novel AI/ML models and algorithms. Experience collaborating in multidisciplinary research environments. Strong oral
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(UESTC) located in Chengdu, China. We are looking for highly qualified and motivated candidates to participate in a research project to develop applications for near term quantum computing devices
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University you will develop, optimise, and oversee our analytics data architecture, tools, and pipelines. You will engineer solutions that allow for the efficient and reliable handling of large scales of data