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Field
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the opportunity to participate in local R&D projects. Qualifications: A doctoral degree in particle physics experiments or a related field is required (or expected by the time of appointment). Strong
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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | 3 months ago
analysis. · Expertise in nonlinear multi-species models, bioeconomic models, or related quantitative methods for natural resource management. · Proficiency in programming languages such as R, Python
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experience with python and/or R is required, experience with one or more of the following skills will be an asset. However, if the individual is willing to learn these skills, there are opportunities to learn
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the use of R and/or Python Basic understanding of statistical modeling, and machine learning Understanding of high-throughput sequencing techniques including whole genome, whole exome, targeted capture, RNA
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knowledge in bioinformatics, machine learning, statistics and programming skills (R, Python, or MATLAB) are required. The ideal candidate should demonstrate a record of publications in the area. Knowledge in
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, mathematics, physics, or a related field. The ideal candidate should demonstrate a record of publications in the area. Strong knowledge in machine learning, statistics and programming skills (R, Python
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. * Proficiency in R, Python, or related software and programming languages. * Experience with big data, linux systems, and remote computing clusters. * Knowledge of genome-wide approaches and other statistical
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model APIs, cloud computing environments, and R for additional statistical analysis. For decision support prototype development and evaluation, web-based user interface design, human-computer interaction
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research associate appointments may not exceed a combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military
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such as R or Python. The ideal candidate will be comfortable working with large geospatial datasets and interested in collaborating as part of a team on various CanN2ONet modelling initiatives. Experience