Displaying 8 of 8 results Python clear
This workshop introduces agent-based modeling, a method for understanding complex social dynamics by simulating interactions among individual agents. Agent-based modeling allows observing social patterns in computer simulations, enabling students to specify rules for agent behavior. The workshop teaches NetLogo for creating models, enabling students to design, analyze, and visualize social dynamics, incorporating empirical data using statistical tools like R or Python.
Interested in learning about the climate system and acquiring computational skills to access, analyze, and visualize climate data? Join Climatematch Academy as a student for our two week virtual program, where you will learn from world-class climate experts and collaborate on team projects with fellow students. Prior experience with climate science is not necessary, however familiarity with a coding language is required.
Immersive training experience teaching skills in numerical modeling, in modern, collaborative, scientific software development, and in the use of open source community cyberinfrastructure.
9AM to 5PM
Taught by Prof. Dr. Petra Ahrweiler (Mainz University; president of ESSA) and Dr. Corinna Elsenbroich (Glasgow University), the course “Policy Modelling” focuses on the substantive problems, theories, and related computational models in a number of core areas of policy modelling. Participants learn to bridge the gap between policy practice and formal models by applying complexity-sensitive computational methods (especially agent-based modelling) using Netlogo and Python.
Compute Canada’s WestGrid is moving its Research Computing Summer School online.
May 7th, 2019 at 12:00PM Mountain Daylight Time
Pre-registration required: https://cuboulder.zoom.us/meeting/register/3564b1479f49678c66858a512be5123a
The GeoClaw software and tsunami modeling[…]
The San Diego Supercomputer Center Summer Institute is a week-long workshop held at the University of California, San Diego that focuses on a broad spectrum of introductory-to-intermediate topics in High Performance Computing and Data Science. The program is aimed at researchers in academia and industry, especially in domains not traditionally engaged in supercomputing, who have problems that cannot typically be solved using local computing resources.