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. The goal is to contribute broadly to research on applications of AI in medicine, and in particular to the development and validation of novel computational language models, algorithms, and tools
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, inferential, and multivariate methods, including principal component analysis (PCA), regression, and machine learning algorithms (e.g., Random Forest), with the aim of integrating various environmental exposure
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learning, and data science, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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The University of British Columbia (UBC) | Vancouver UBC, British Columbia | Canada | about 2 months ago
optimization and coordination algorithms for BC Hydro–cVPP interaction and evaluate grid performance through simulated case studies, and analyze service offerings and pricing via case studies and apply social
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, scikit-learn); Familiarity with advanced methods such as causal forests, uplift modeling, instrumental variables, or difference-in-differences is highly valued. WORK PLAN
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experience Evidence of experience with public-patient involvement and stakeholder engagement Track record in scientific publication and dissemination Experience with Machine Learning Algorithms and Artificial
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intelligence, to support different aspects of marine science, with opportunities to expand to other areas. This is a 12-month, non-tenure-accruing position, available in the Systems and Imaging Laboratory (SAIL
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such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological
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complex geometries represents a hazard. It is also relevant to compare the SoK for similar systems using different fuels, and to explore the predictive capabilities of consequence models through blind