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, demonstrated experience of coding in programming languages such as R and Python is considered particularly advantageous. Examples of computationally intensive methods central to IAS and IDA are data-driven text
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, decentralization and mission execution. The RAI team has a strong European participation in multiple R&D&I projects, while RAI was also participating in the DARPA SUB-T challenge with the CoSTAR Team lead by NASA
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large databases is required. The candidate is expected to partake in design of the epidemiological studies, statistical work (SPSS but also other programs such as R), presentations and preparation
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. Programming skills in R is a requirement, and programming skills in dynamic modelling in other languages is a merit. Fluency in spoken and written English is a requirement. Qualifications: PhD degree in ecology
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. Qualifications are preferably exemplified in the application. Required qualifications: Solid insights in marine ecology Programming skills in Fortran, Python and R Demonstrated skills in statistical analyses and
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Excellent skills in bipartite network analysis Previous research experience in interaction inference Good skills in R programming Experience in preparing and handling large datasets integrating data from
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plan and conduct data processing and statistical analyses. Knowledge of statistics and programming (e.g., R) is therefore a requirement. The candidate is expected to compile research results, interpret
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, demonstrated experience of coding in programming languages such as R and Python is considered particularly advantageous. Examples of computationally intensive methods central to IAS and IDA are data-driven text
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are seeking a motivated and skilled Postdoctoral Researcher to join the Research and Development (R&D) team at the Uppsala node of the National Genomics Infrastructure (NGI), part of SciLifeLab
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of the project, through development of the R package treepplr (www.github.com/treeppl/treepplr ). The work will include development and implementation of methods to test model adequacy, inference diagnostics