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. Qualifications: PhD in machine learning, with experience in applications in computer vision or medical image analysis. Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA
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Research Assistant 2 position is based in Prof. Tal Arbel's research lab in the department of Electrical and Computer Engineering. This position provides research, technical, and administrative support for
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: Literature review, data collection, input variable selection, preliminary model building for machine learning based streamflow forecasting. Qualifications: BEng; Very strong ability in coding and ML/DL
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McGill University | Winnipeg Sargent Park Daniel McIntyre Inkster SE, Manitoba | Canada | 28 days ago
the supervision of the immediate supervisor, you are expected to develope computer code for implementing research ideas, participate and lead weekly research progress meetings and write research articles
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: Literature review, data collection, input variable selection, preliminary model building for machine learning based streamflow forecasting. Qualifications: BEng; Very strong ability in coding and ML/DL
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first author publications in reputable peer-reviewed journals Advanced quantitative skills (e.g., advanced stats [MLM], machine learning, data mining). Willingness to develop desired skills (see directly
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spatial omics datasets. The position will also contribute to multi-modal data integration efforts that combine imaging, genomics, and machine learning approaches. Key Responsibilities Data Processing
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control subjects based on diffusion MRI images and functional MRI responses. Duties include: Developing machine-learning and/or deep learning pipelines for classifying patients of optic neuropathies and
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repositories programmed in Python, Pytorch, LangChain using git repo. Develop clean, readable, and maintainable public code using object-oriented programming principles in Java and Python. Apply machine learning
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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