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Council of Canada). The research will focus on applying, developing, and implementing novel statistical methods for causal inference, integrative data analysis, and machine learning with large
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Pacific Institute for the Mathematical Sciences | Northern British Columbia Fort Nelson, British Columbia | Canada | 1 day 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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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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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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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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McGill University | Winnipeg Sargent Park Daniel McIntyre Inkster SE, Manitoba | Canada | 22 days 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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to an open-ended academic position in which the holder can form a research group, apply for externally funded research as a principal investigator, and teach. In addition: you must have completed all
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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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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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of Dr Benoit Gosselin (Université Laval), Guillaume Lajoie (UdeM) and Marco Bonizzato (Polytechnique). It integrates the use of machine-learning approaches to optimize neurostimulation, automation