SCINet, the AI-COE, and other providers regularly host a variety of events and trainings. Information on how to attend these events will be posted on this page closer to the event date. You might want to take a look at events we’ve hosted in the past, too.
If you have a training request that is not being offered at this time, please complete this short training request form to let us know.
Keep an eye on this page for more info about upcoming events!
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SCINet Corner · Introduction to SCINet
An overview of SCINet and the computing and training resources we offer.
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Using Globus to share data with external collaborators
Do you have large files on SCINet systems that you would like to share with external collaborators who don’t have SCINet accounts? You will soon be able to do this by using Globus, the same web-based interface recommended for other data transfers to, from, and among SCINet systems!
We will be hosting a webinar Friday, January 23, 2026, 3-4 PM ET to give an overview of what kinds of sharing are supported and a demonstration of how to request and set up file sharing.
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RNA-seq analysis with Galaxy
In this workshop, participants will work through a complete RNA-seq analysis using SCINet’s Galaxy interface.
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The Carpentries Unix, Git, and Python Workshop
The SCINet Office, in collaboration with ARS’s certified Carpentries instructors, is offering a Carpentries workshop that will teach participants the Unix command line, version control with Git, and Python programming.
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Using large language models (LLMs) on SCINet's supercomputers
Large language models (LLMs), a key technology behind well-known tools like ChatGPT and Microsoft Copilot, have a multitude of applications in agricultural research. SCINet’s high-performance computing resources provide an excellent environment for research use of LLMs, and this workshop will equip you with the knowledge and tools you need to take advantage of LLM capabilities in your research.
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Practicum AI · Transfer Learning
This workshop provides the foundational concepts and practical applications of transfer learning, a powerful technique in deep learning that allows AI models to leverage pretrained knowledge to improve performance on new tasks. The sessions will cover different types of transfer learning techniques, such as feature extraction and fine-tuning. This includes hands-on experience in applying these techniques to computer vision and language models.