A practical 5-day course teaching biologists how to code in Python. Learn to manipulate, analyze, and visualize biological data using real-world examples. Ideal for beginners with no prior programming...

Why does geography matter for social science research? Join our free 90-minute online seminar designed for PhD students, postdocs, and professional researchers interested in spatial thinking, geographic data, and policy-relevant empirical work. This guided, code-free showcase explains why location is analytically consequential, from spatial dependence and neighbourhood effects to territorial boundaries and place-based inequality, and shows how ignoring geography can distort inference and obscure mechanisms. Through finished maps, real results, and published studies, you’ll see how spatial analysis can change research conclusions in areas such as electoral behaviour and policy diffusion, without writing a single line of code. The session also introduces how R can function as a complete Geographic Information System (GIS), offering a free and reproducible alternative to proprietary GIS tools. No coding experience or prior GIS knowledge is required. Participants receive access to the live Zoom seminar, 30-day recording access, slides, a curated reading list, showcase maps and results, a 30-day Q&A forum, and a certificate of completion. #AppliedResearch #ComputationalSocialScience #DataAnalytics #DataScience #Economics #Geography #PoliticalScience #QuantitativeMethods #ResearchMethods #Scientificprogramming #SocialSciences #SpatialData #Statistics #PublicPolicy #R #Research #ResearchTraining #Instats

Join our online seminar on R programming for contemporary statistical inference and machine learning, designed for PhD students and professional researchers working with high-dimensional biological, clinical, imaging, or observational data. Led by Nikolay Oskolkov, Group Leader (PI) at LIOS, this seminar combines methodological rigor with hands-on R implementation, covering reproducible workflows, linear models, LASSO, Partial Least Squares, discriminant analysis, PCA, tSNE, UMAP, clustering methods, neural networks, Random Forests, model diagnostics, feature selection, and interpretability. Participants will strengthen their ability to design, implement, and report complex analyses in publication-ready research pipelines while gaining a deeper understanding of both classical inference and modern machine learning approaches. The seminar is delivered via Zoom, with recordings, slides, code, data, and a monitored Q&A forum available for 30 days after the seminar, making it easy to attend live or asynchronously. An official Instats certificate of completion is provided, with ECTS Equivalent points available where applicable. #BayesianStatistics #Bioinformatics #Biostatistics #DataScience #MachineLearning #Scientificprogramming #Statistics #ComputerScience #SoftwareEngineering #RStudio #Research #ResearchTraining #Instats

How can geographic thinking strengthen social science research? Join this free 90-minute online seminar for PhD students, postdocs, and professional researchers to explore why location matters when studying spatial dependence, neighbourhood effects, territorial boundaries, uneven development, electoral behaviour, policy diffusion, and place-based inequality. This guided, code-free showcase introduces key concepts such as Tobler’s Law and demonstrates how spatial analysis can change research conclusions, improve inference, and increase policy relevance, all through finished maps, results, and published studies rather than live coding. You’ll also see how R can function as a complete Geographic Information System (GIS), offering a free and reproducible alternative to proprietary tools. No coding experience or prior GIS knowledge is required. Participants receive access to the live Zoom session, a 30-day recording, presentation slides, a curated reading list, showcase maps and results, a 30-day Q&A forum, and a certificate of completion. This is an ideal entry point for researchers who work with geographic data or want to understand whether spatial analysis could strengthen their next project. #AppliedResearch #ComputationalSocialScience #DataAnalytics #DataScience #Economics #Geography #PoliticalScience #QuantitativeMethods #ResearchMethods #Scientificprogramming #SocialSciences #SpatialData #Statistics #PublicPolicy #Sociology #R #Research #ResearchTraining #Instats

Want to work more confidently with spatial data in R? This online seminar introduces the foundational concepts and practical workflows researchers need to use R as a free, reproducible GIS, covering spatial objects, coordinate reference systems, geographic data structures, map-based visualisation, troubleshooting, and reusable scripts for your own research. Designed for PhD students, postdoctoral researchers, and professional researchers across the social sciences, economics, geography, political science, data science, and computational social science, the seminar is especially relevant for projects involving inequality, diffusion, territory, voting, development, environmental exposure, and other place-based questions. Led by Fernando De la Cuesta, who brings expertise in applied and public economics as well as extensive experience teaching spatial analysis, the workshop combines live, example-driven R workflows with opportunities to apply techniques to your own variables and leave with commented scripts for future use. Livestream participants join via Zoom, with recordings, materials, data, and an online Q&A forum available for 30 days after the seminar; on-demand access is also available. Participants receive an official Instats certificate of completion, with ECTS Equivalent points available where shown. #AppliedResearch #ComputationalSocialScience #DataAnalytics #DataScience #Economics #Geography #PoliticalScience #QuantitativeMethods #ResearchMethods #Scientificprogramming #SocialSciences #SpatialData #Statistics #ggplot2 #GIS #R #Rmarkdown #RStudio #tidyverse #Research #ResearchTraining #Instats

Join our online seminar on R programming for contemporary statistical inference and machine learning, designed for PhD students and professional researchers working with high-dimensional biological, clinical, imaging, or observational data. Led by Nikolay Oskolkov, Group Leader (PI) at LIOS, this workshop connects methodological rigor with hands-on, reproducible research workflows in R and RStudio. Participants will explore key topics including linear modelling, frequentist and Bayesian reasoning, LASSO, Partial Least Squares, discriminant analysis, PCA, tSNE, UMAP, clustering methods, neural networks, Random Forests, diagnostics, model comparison, interpretability, and publication-ready reporting. The seminar is ideal for researchers who want to strengthen both their theoretical understanding and practical coding skills for credible, transparent, and peer-review-ready analysis. All livestream sessions are delivered via Zoom, with recordings, slides, code, data, and other materials available for 30 days after the seminar, along with access to an online Q&A forum monitored by the expert. Participants receive an official Instats certificate of completion, with ECTS Equivalent points available where shown. #BayesianStatistics #Bioinformatics #Biostatistics #DataScience #MachineLearning #Scientificprogramming #Statistics #ComputerScience #SoftwareEngineering #RStudio #Research #ResearchTraining #Instats

Advance your research skills with our online seminar on R programming for contemporary statistical inference and machine learning, designed for PhD students and professional researchers working with high-dimensional biological, clinical, imaging, or observational data. Led by Nikolay Oskolkov, Group Leader (PI) at LIOS, this seminar combines methodological rigor with hands-on R implementation, covering reproducible workflows, linear modelling, LASSO, PLS, LDA, PCA, tSNE, UMAP, clustering methods, neural networks, Random Forests, model diagnostics, feature selection, and publication-ready reporting. Participants will gain both the conceptual foundations and practical coding skills needed to design, implement, interpret, and report complex analyses in R. All livestreaming sessions take place via Zoom, with recordings, slides, code, data, and a monitored Q&A forum available for 30 days after the seminar, making it easy to attend live or asynchronously. An official Instats certificate of completion is provided, with ECTS Equivalent points available where shown. #BayesianStatistics #Bioinformatics #Biostatistics #DataScience #MachineLearning #Scientificprogramming #Statistics #ComputerScience #SoftwareEngineering #RStudio #Research #ResearchTraining #Instats

Join an intensive online seminar with Nikolay Oskolkov (Group Leader, LIOS) that treats R as an integrated toolkit for modern statistical inference and machine learning—covering penalised regression (LASSO), PLS, discriminant analysis, PCA, t-SNE, UMAP, clustering (hierarchical, k‑means, GMM, spectral, density- and graph-based), plus building neural networks and Random Forests from first principles in R. Designed for PhD students and researchers working with high-dimensional biological, clinical, imaging or observational data, the workshop balances concise theory with hands-on RStudio coding to build reproducible, publication-ready workflows, rigorous diagnostics, model comparison and interpretable results, and to integrate modelling, code and outputs into reproducible reports suitable for peer review. All sessions are livestreamed via Zoom, recordings and all materials (code, data, slides) are available for 30 days after the seminar with a monitored Q&A forum for follow-up questions; on-demand access is also offered. Participants receive an Instats certificate of completion (ECTS-equivalent points indicated for European students). #BayesianStatistics #Bioinformatics #Biostatistics #DataScience #MachineLearning #Scientificprogramming #Statistics #ComputerScience #SoftwareEngineering #RStudio #Research #ResearchTraining #Instats

Advance your R-based inference and machine learning skills in a hands-on online seminar led by Nikolay Oskolkov (Group Leader, LIOS) designed for PhD students and researchers working with high-dimensional biological, clinical, imaging or observational data. The course pairs concise theory with practical R/RStudio implementations to cover penalised regression (LASSO), PLS and LDA, PCA, tSNE and UMAP, clustering from hierarchical and k‑means to GMMs, spectral, density‑ and graph‑based methods, plus coding core algorithms from first principles (small neural nets, Random Forest), diagnostics, gradient‑descent optimisation, feature selection, model comparison, and reproducible workflows for publication-ready analyses. Live sessions are delivered via Zoom, all recordings and materials are available for 30 days after the seminar (on-demand access available after activation), a Q&A forum is monitored for 30 days post-seminar, and participants receive an Instats certificate of completion (ECTS equivalent shown where applicable). Ideal for researchers who need rigorous, transparent code and interpretable results — enroll or contact us to learn more. #BayesianStatistics #Bioinformatics #Biostatistics #DataScience #MachineLearning #Scientificprogramming #Statistics #ComputerScience #RStudio #Research #ResearchTraining #Instats

Join our three-day intensive Python workshop for PhD students, professors, and researchers, taught by Dr. Denis Dusseldorf — no prior programming knowledge required. You’ll gain a solid, research-centered foundation (language fundamentals, functions, classes, modularisation, package management) and hands-on experience with numpy, matplotlib and pandas so you can clean, visualise and analyse real datasets and confidently adapt or debug AI-generated code. Sessions mix compact lectures, live coding, exercises and Q&A (bring your own data/problems), and all live seminars include Zoom streaming, recordings and materials for 30 days plus a monitored Q&A forum; participants receive a certificate of completion (ECTS-equivalent noted where applicable). Spaces are limited — contact us or visit our site to learn more and register. #Scientificprogramming #Education #ComputerScience #DataScience #Python #Research #ResearchTraining #Instats

Ready to make Python a productive part of your research workflow? Join Dr. Denis Dusseldorf for a three-day intensive online workshop designed for PhD students, professors, and professional researchers — no prior programming experience required. You’ll learn Python fundamentals, functions, classes and modularisation, plus package management and essential data-analysis tools (numpy, matplotlib, pandas) through concise theory, live coding, hands-on exercises and Q&A so you can adapt or debug AI-generated code and build reproducible workflows. Bring your own datasets or problems; all sessions are livestreamed via Zoom, recordings and materials (including a monitored Q&A forum) are available for 30 days afterward, and participants receive a certificate of completion (ECTS-equivalent points shown for European students). Spaces are limited—sign up to gain practical, immediately applicable Python skills for academic and industry research. #Scientificprogramming #Education #ComputerScience #DataScience #Python #Research #ResearchTraining #Instats

📣 Call for Symposia Share your research or emerging topics with the #biomaterials community. Submit your symposium proposal by 30 November 2025. Join us in celebrating 50 years of innovation at E...