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programming skills are essential, with a preference for candidates proficient in Python. Experience with dark matter or neutrino experiments is desirable. What We Offer: Competitive Salary: A minimum of 300,000
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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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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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, generally with python. Duties also include: presenting at group meetings, attending conferences, mentoring students, and writing journal articles. Qualifications Basic qualifications Ph.D. in computational
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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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): Computer Science or Informatics: Proficiency in programming and software development with a habit for robust unit testing. Our group mainly develops software in a Python + SQL environment with use of large language
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experience using and advancing data analytics and modeling, which may include using FEFF, Python or other modeling/programming languages. • Your experience is demonstrated through publications, GitHub
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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