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Field
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may include but are not limited to: algorithm and software development; application or development of computational or statistical methods; data analysis; modeling; statistics and machine learning
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-performance computing, machine learning models (eg. LLM), probabilistic models for data, novel techniques for making measurements, visualization tools, and community-oriented foundational software tools. Please
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness
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interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional neuroimaging data. Specific activities may include
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of the project is to provide a timely update and modifications of this model, as new science and methods have evolved since its development. Learning Objectives: The selected fellow in this project will have the
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experience of the candidate At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and
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. Proficiency in using PyTorch and other machine learning libraries for developing and implementing machine learning models is essential. Ability to prepare comprehensive written reports, including the use
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to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome
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mitigation, dynamic data integration, validation, and interoperability. The research will involve applying various techniques, such as representation learning methods and transformer-based models, to tackle