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distributed decision-making. Applicants must have a PhD in Electrical Engineering, Mechanical Engineering, Computer Engineering, Applied Mathematics, Mathematics, or a closely related discipline, and are within
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Associate to build a research program to tackle these key issues, reporting directly to Professor David Edwards. The successful candidate will work with large datasets, including leveraging large-scale global
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electrodes, thermoelectric materials and ferroelectric relaxors. The computational environment The postdoc will be scientifically embedded in Professor Georg Madsen's Research Unit of Theoretical Chemistry at
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of two experts in computational microscopy and machine learning systems, blending expertise in advanced bioimage informatics and parallel computing: Dr. Min Xu – Affiliated Associate Professor of Computer
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– Affiliated Associate Professor of Computer Vision, MBZUAI & Associate Professor, Carnegie Mellon University. Dr. Xu directs a dynamic research laboratory focused on computational biology, bioimage informatics
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the distribution of time varying across the project: 60-70% Research and implementation: design the Innovation Topology Embedding; develop and scale the topology-computation pipeline; integrate topological features
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is to be the “EO innovation hub” connecting EO with a growing ecosystem of disruptive and transformative innovations such as AI, machine learning, quantum computing, edge computing, metamaterials and
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opportunities, and social activities. A work environment that values professionalism, diversity, equality, and work-life balance. Place of Work The position is associated with two permanent workplace locations in
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
computing environments (e.g., GPUs, distributed training). Experience mentoring graduate or undergraduate students. Special Physical/Mental Requirements NA Special Instructions For information on UNC
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carcasses for analysis. Your task will be to explore what we can learn from the beached bird data. Topics include the distribution of mortality in time and space, and among species; causes of death, as