Join Prof. Melvyn Weeks (University of Cambridge) for a two-day intensive workshop that blends modern machine learning with rigorous econometric methods to teach causal inference—not just prediction—for PhD students, academics and research professionals. Over eight hands-on sessions you’ll learn high-dimensional regularisation (lasso, double lasso), double selection and double/debiased ML, tree- and forest-based treatment-effect methods, connections between classical identification strategies and ML, and practical workflows in Python, R and Stata using worked notebooks and sample data. All seminars are livestreamed via Zoom, with recordings, code and materials available for 30 days after the course and an expert‑monitored Q&A forum for the same period; participants receive an Instats certificate on completion (ECTS-equivalents shown where applicable). Gain the tools and confidence to move from correlations to credible causal claims in your own research—find out more and secure your place today. #SocialSciences #HealthSciences #NaturalSciences #SocialTheory #ComputerScience #Economics #Statistics #DataScience #ChatGPT #Python #R #Stata #Research #ResearchTraining #Instats

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Join Dr. Niamh Mimnagh (PR Stats) for an intensive 10-day, hands-on workshop that takes you from foundational Bayesian concepts to advanced multilevel, temporal, spatial and multivariate models tailored to ecology (and allied health and social sciences) using brms in R with Stan under the hood. Over 30 sessions (75 minutes each) you’ll move from Bayes’ theorem and MCMC diagnostics through GLMs, random intercepts/slopes, GP and CAR spatial models, joint species distribution models, model criticism and visualization, and leave with reproducible, publication-ready workflows you can use in theses, manuscripts and grant proposals. Designed for PhD students, academics and professional researchers with working R skills (no prior Bayesian experience required), the course blends short lectures, extensive coding labs, case studies, live debugging/Q&A, a monitored forum and 30-day access to recordings/materials; participants receive an Instats certificate on completion (ECTS equivalent noted for European students where indicated). Interested? Visit the Instats site to learn more and register. #HealthSciences #NaturalSciences #SocialSciences #ComputerScience #DataScience #Biostatistics #Ecology #Statistics #R #Research #ResearchTraining #Instats

Machine learning is now an essential research tool — join Dr Niamh Mimnagh (PR Stats) for a five-day, hands-on workshop introducing practical ML in R for PhD students, academics and professional researchers. Through live coding, worked examples and guided exercises you’ll learn modern workflows (tidymodels, caret), implement supervised and unsupervised methods (glmnet, xgboost, nnet, clustering, PCA/t-SNE), perform hyperparameter tuning and resampling, and build interpretable, publication-ready models with reproducible code and presentation-ready figures using ggplot2 and RMarkdown. The course also covers regularisation, model evaluation (RMSE, AUC, F1), handling missing data and class imbalance, time-series features, and interpretability tools (variable importance, partial dependence, SHAP-style), plus practical guidance on when ML adds value versus traditional approaches. All sessions are livestreamed via Zoom, recordings and materials are available for 30 days with a monitored Q&A forum, and participants receive a certificate of completion (ECTS-equivalent points shown for European students). Ideal for researchers who want to design and justify end-to-end ML analyses for publication — limited spaces, find out more and enroll today. #HealthSciences #NaturalSciences #SocialSciences #Education #ComputerScience #Statistics #DataScience #R #Research #ResearchTraining #Instats

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Apply for Research Associate in Geodesy at GFZ Helmholtz, Potsdam. Full-time, 2-year role in space physics & satellite data. Deadline: 15 Oct

Join Prof. Melvyn Weeks (University of Cambridge) for an intensive two-day workshop on causal machine learning designed for PhD students, academics and professionals who want to move beyond correlation to credible causal inference. Over eight hands-on sessions you will learn high-dimensional regularisation (lasso, double lasso), Double Selection and Double/Debiased ML (DDML), tree- and forest-based treatment-effect estimation, and how to connect classical econometric identification with modern ML—using worked notebooks and sample data in Python, R and Stata. Sessions are livestreamed via Zoom, all recordings and materials (code, data and slides) plus a monitored 30-day Q&A forum are available after the course, and participants receive a certificate of completion (ECTS-equivalent where indicated). This workshop builds on material taught with Jeff Wooldridge and will give you the conceptual shift and practical toolkit to apply rigorous causal ML methods in your research—register to secure your place. #SocialSciences #HealthSciences #NaturalSciences #SocialTheory #ComputerScience #Economics #Statistics #DataScience #ChatGPT #Python #R #Stata #Research #ResearchTraining #Instats