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R Programming

R Programming
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R Programming

Choose a category below to begin. Each category opens into focused areas, and selecting a focused area lets you pick your final internship variant (your preferred track, mode and duration).

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Showing 1–20 of 50

AI R Bioinformatics Research
Internship studying bioinformatics in R with workflows across sequence, expression, and annotation. Applied sessions reinforce each technique.
5 focused areasClick to view more details →
Machine Learning R Statistical Modeling Research
Internship studying statistical modelling in R with ML methods alongside classical inference. Guided practice with real datasets throughout.
5 focused areasClick to view more details →
AI R Epidemiology Research
Internship studying epidemiology in R with analysis of study design, modelling, and visualisation. Practical exercises anchor every concept taught.
5 focused areasClick to view more details →
AI R Genomics Pipeline Research
Internship studying genomics pipelines in R with workflows from counts through biological interpretation. Interns work with realistic case datasets.
5 focused areasClick to view more details →
AI R Single-Cell Analysis Research
Internship studying single-cell analysis in R with clustering, annotation, and trajectory workflows. Interns practise on genuine research problems.
5 focused areasClick to view more details →
AI R Clinical Trial Analysis Research
Internship studying clinical trial analysis in R with reproducible reporting and statistical modelling. Guided practice with real datasets throughout.
5 focused areasClick to view more details →
AI R Survival Analysis Research
Internship studying survival analysis in R with modelling of time-to-event and censored data. Applied sessions reinforce each technique.
5 focused areasClick to view more details →
AI R Machine Learning Research
Internship studying machine learning in R with model building, tuning, and honest evaluation. Mentor-led sessions build applied skill.
5 focused areasClick to view more details →
AI R Environmental Data Research
Internship studying environmental data analysis in R with spatial, temporal, and monitoring workflows. Includes mentored hands-on analysis sessions.
5 focused areasClick to view more details →
AI R Metabolomics Research
Internship studying metabolomics in R with processing, statistics, and pathway interpretation. Hands-on work runs alongside theory modules.
5 focused areasClick to view more details →
R Shiny Interactive Dashboard Development
Design and build interactive web-based dashboards using Shiny to visualize complex datasets and research findings in real-time.
5 focused areasClick to view more details →
R Time Series Forecasting Analysis
Develop predictive models using ARIMA, exponential smoothing, and neural networks to forecast temporal patterns in research data.
5 focused areasClick to view more details →
R Spatial Data Analysis Research
Analyze geographic and spatial datasets using sf, raster, and ggplot2 packages for mapping and geospatial research applications.
5 focused areasClick to view more details →
R Network Analysis Graph Research
Investigate complex network structures and relationships using igraph and tidygraph packages for social and biological network studies.
5 focused areasClick to view more details →
R Text Mining NLP Research
Extract insights from unstructured text data using tokenization, sentiment analysis, and topic modeling techniques in R.
5 focused areasClick to view more details →
R Causal Inference Research Methods
Apply propensity score matching, instrumental variables, and causal forest methods to estimate treatment effects in observational studies.
5 focused areasClick to view more details →
R Bayesian Statistical Modeling
Implement Bayesian inference using Stan, JAGS, and prior specification techniques to quantify uncertainty in research parameters.
5 focused areasClick to view more details →
R High-Dimensional Data Analysis
Apply dimensionality reduction, feature selection, and regularization techniques to handle datasets with thousands of variables.
5 focused areasClick to view more details →
R Functional Data Analysis Research
Develop methods to analyze data viewed as functions using functional principal component analysis and functional regression techniques.
5 focused areasClick to view more details →
R Microbiome Data Analysis
Process and analyze 16S rRNA sequencing data using phyloseq and dada2 packages for microbiome composition studies.
5 focused areasClick to view more details →