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Field
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-solving abilities. Strong written and oral communication skills in both English and French. Additional Skills (Preferred): Experience with environmental data analysis (R, Python, multivariate statistics
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have a Ph.D. in economics, public policy, education, or a related field by the start date, strong quantitative and econometric skills, experience coding (in Stata, Python, R, and/or SAS), and an interest
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or equivalent Skills/Qualifications - PhD in bioinformatics or related subjects - Expertise in python coding - Experience and good understanding of neural networks and machine learning - Fluent written and spoken
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the core Python application, ii) major contributor to the project ecosystem design and implementation, iii) coordinator of working groups, and iv) major contributor to project presentations and publications
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related to research, teaching, and departmental operations Requirements: • A completed doctoral degree in chemistry, bio technology or a related field. • Strong programming skills in Python, R
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the oceanographic community including MatLab, Ferret, and/or Python. ● The incumbent must have a proven publication record, strong scientific writing and computer skills, and the ability to communicate science to
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(Python), and be supported by high quality publications About the Institute The role will be based in the Digital Environment Research Institute (DERI), Queen Mary’s flagship University Research Institute
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interpreting omic-seq data sets, with Unix/Linux operating systems, with RAID and server systems; should have good scripting/programming skills (R, Python, Bash, Next-Flow, etc.); and should have a good command
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(Python), and be supported by high-quality publications. About the Institute The role will be based in the Digital Environment Research Institute (DERI), Queen Mary’s flagship University Research Institute
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(e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a