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training and guidance to junior undergraduate and graduate students. Education: A PhD in Neuroscience, Computational Neuroscience, Machine Learning in image analysis, or a related field, with significant
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The SSHRC-funded HubMeta Lab project is undertaking the largest-ever meta-analysis of SME growth studies (over 2,500 full-text articles coded to date). We have uncovered extraordinary variability in
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process design, computational modeling, and techno-economic analysis. The start date for the position is 1 October 2025, or shortly thereafter. The salary for the position is $50,000 per year. We encourage
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Excellent organizational, communication, and teamwork skills Experience with dynamic causal modelling (DCM), machine learning or related computational approaches are an asset Roles and Responsibilities
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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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to the role (including OpenSpecimen database and Genemapper software) and capable of quickly learning and utilizing additional computer programs as needed
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Pacific Institute for the Mathematical Sciences | Northern British Columbia Fort Nelson, British Columbia | Canada | 3 months 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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analysis at unprecedented resolution in axolotls and flies. The positions require both overlapping and distinct skill sets as outlined below: All applicants must have a: - Ph.D. in Molecular Biology
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Summary This position will provide technical support to data management plan development, data management, and assist Post-Doctoral Fellows with the development of machine learning methods. Organizational