This three-hour workshop focuses on learning the basics of python programming including data types, conditionals,...

Python for Big Data Analytics (Webinar)   The workshop will review several techniques for addressing the...

Hands-on Training - Machine Learning with Python and Jupyter In the last few years, both...

Hands-on Training - Deep Learning with Python and Jupyter Deep Learning (DL) outperforms Machine Learning (ML)...

Data Visualization with Python Jupyter Notebooks – A Hands-on Introduction Registration: Required (Webinar link will...

4th GPU Hackathon at Princeton (Virtual event)June 1, 2022 (Prep day)June 6-8, 2022 (Main event)Contact:...

Please join the June Research Computing and Data CG Open Call. As a reminder, our Open Calls are held on the...

Please join ERN at the PEARC'22 Conference next week for our official co-located event, ERN:...

Linux Clusters Institute (LCI) workshopIntroduction to Linux Cluster System Administrationat Dartmouth College, Hanover NHContact: Leslie...

CI Compass is pleased to announce its next installment in our webinar series: Commercial Cloud Solutions...

The 26th Annual IEEE High Performance Extreme Computing Conference (HPEC ’22) will be held as...

Materials Discovery is one of the research areas where gaining a deeper understanding of the workflows, research computing and data requirements, collaborations, and challenges will enable the ERN to have the broadest impact across multiple research disciplines, pedagogical approaches, senior level college and university administrators, and other organizations within the region and beyond. Researchers in materials discovery are realizing their traditional data-intensive HPC workflows are reaching the limits of original progress. For this reason, they are looking to new paradigms that include convergence of HPC and Machine Learning (ML) methodologies, algorithm development, and novel ways to access the data distributed across multiple institutions used in training systems as promising approaches to overcome the major computational performance limitations they are faced with. Exploratory conversations with Penn State, Rutgers, SUNY Buffalo, MIT, and others suggest that Materials Discovery offers an attractive testbed for advanced cyberinfrastructure of the sort the ERN can offer through future funding opportunities such as the Mid-Scale RI-1 program. As with Cryo-EM/Cryo-ET, this session will explore possibilities for extending collaborations to include other institutions as well as the community of Research Computing and Networking organizations.
Please click on the event title to view the agenda on the next webpage.