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their publication record and research statement. Preferred skills will include data science methods such as high performance computation, data management skills, and programming in R. Individuals also must be within
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data. Proficiency in ML frameworks (experience with LLavaMed, EchoPrime and DNABERT considered an asset) and programming languages such as Python or R. Experience working with medical imaging or genomic
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marine ecosystems preferred Experience conducting field work is an asset (but not required). Required experience with statistical analysis in R. Project advising team: UBC PI - Dr. Chris Harley https
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Develop new statistical models to jointly analyze multi-species data from a broad range of data sources. Develop reproducible code (and potentially an R package) to apply this method to the data. Analyze
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communicate effectively, both verbally and in writing Must be proficient with Microsoft operating systems, Excel, Access, Word Demonstrated ability to use statistical software including SAS and R Ability
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author publication(s) in journals relevant in your field and/or shared as preprints Strong statistical foundation and expertise in R or python for data analysis Fluent in written and spoken English and
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desirable. Proficient in Python, R, and ML libraries such as PyTorch or TensorFlow. Strong communication and collaboration skills; ability to work independently and as part of a team. Willingness to respect
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reports from a large clinical dataset Using statistical analysis packages (e.g., SAS, R, SQL) to analyze large healthcare administrative datasets Participating in quantitative study design and
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-motivated and independent work skills Ability to work as part of a team. Preference will be given to applicants with intermediate or advanced computer skills in the following software packages: R, Stata
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to work as part of a team. Preference will be given to applicants with intermediate or advanced computer skills in the following software packages: R, Stata or SAS (quantitative data analysis software), MS