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motivated Neuroscience postdoctoral fellow. In addition to neuroscience research experience, having familiar with machine learning/AI/ big data processing will be an asset. A major part of this PDF
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Pacific Institute for the Mathematical Sciences | Northern British Columbia Fort Nelson, British Columbia | Canada | 9 days ago
discretizations and/or machine learning methods. The ideal candidate should have a strong background in numerical analysis, scientific computing, and/or scientific machine learning. We are particularly interested
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this will include: Demonstrated expertise in data analysis and simulation Familiarity with C++; and proficiency in the use of ROOT and Geant4, and interest in machine learning techniques Knowledge
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approaches (based on functional programming abstractions) to optimize the implementation of machine learning models and other digital signal processing algorithms on a specific FPGA architecture to fit within
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are not limited to superconducting quantum circuits, circuit QED, quantum error correction, microwave quantum optics, variational quantum algorithms, and the application of machine learning to quantum
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Overview On this page Objective The Canada Postdoctoral Research Award (CPRA) program recognizes and supports the next generation of outstanding innovators, knowledge workers, creative thinkers and
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McGill University | Winnipeg Sargent Park Daniel McIntyre Inkster SE, Manitoba | Canada | 2 months ago
fluorescence data. Developing machine learning methods to optimize data collection. In addition, the project is committed to developing open source tools that benefit the imaging community. The applicant will
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with multivariate statistics, machine learning, and/or remote sensing would be an asset. Experience and education: Ph.D. degree in geography, agriculture/agronomy, environmental science, or a related
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: - To lead the computational part of a collaborative project on AI-assisted design of OPVs - To become knowledgeable in the field of OPVs and the relevant simulations - To learn relevant machine learning
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sciences.Tackling key problems in biology will require scientists trained in areas such as chemistry, physics, applied mathematics, computer science, and engineering. Proposals that include deep or machine learning