ANNOUNCING THE OSG SCHOOL 2026!

https://osg-htc.org/school-2026/

Could you transform your research with extra computing capacity beyond your laptop or lab computers?  Do you need help managing your research computing workloads with automation?  If you run or support research workloads that can be described by lists of independent computing tasks, we can help!  For example: https://osg-htc.org/services/open_science_pool.html

We are seeking applicants for the OSG School 2026, to be held July 13-17 at the beautiful University of Wisconsin-Madison.  Apply by March 20!  https://osg-htc.org/school-2026/

And consider attending Throughput Computing Week 2026, in-person in Madison or remotely: https://osg-htc.org/htc26

The OSG School teaches you how to use high-throughput computing effectively and get a research workload up and running.  We use lectures, demos, hands- on exercises, personal consulting with OSG experts, and even roleplaying.

Past participants have come from physics, chemistry, engineering, math, bio and life sciences, earth sciences, agricultural and animal sciences, social sciences, economics, medicine, and more.

Ideal candidates are:
* Researchers (especially graduate students and post-docs) for whom large- scale computing is a key part of the research process;
* Research support staff who work with current or potential users of high throughput computing;
* Instructors (at the post-secondary level) who are ready to integrate high throughput computing into their research based curriculum.

We aim to pay all base travel, hotel, and food costs for applicants whom we invite to attend.
This is an in-person event, and we strive to provide a healthy environment for all.

DETAILS
* Application Period (OPEN NOW): 23 February - 20 March 2026
* OSG School: 13-17 June 2026
* Web: https://osg-htc.org/school-2026/
* Email: school@osg-htc.org

Today, educational content is fragmented across PDFs, websites, lecture materials, assignments, notebooks, and datasets. Course materials are often scattered between Canvas, personal websites, Git repositories, cloud drives, and public data repositories, making them difficult to discover, reuse, share, or scale beyond a single instructor’s workflow.

The National Data Platform (NDP) integrates data discovery, collaborative workflows, and scalable computing. As part of this ecosystem, the Education Hub curates modular, reusable learning content, enabling students and educators to directly engage with real-world datasets, models, and tools.

Workshop Objectives

This three-day workshop will bring together educators, administrators, and researchers interested in shaping the next generation of AI-enabled, data-driven learning experiences. During the workshop, you will learn about the NDP Education Hub activities as pathways to share your current teaching workflows using NDP as compute-ready educational content that is discoverable, reusable, and reproducible, and how you can translate your current instruction materials to utilize available resources (e.g. NAIRR Classroom). You will learn about the National Data Platform and NAIRR Classroom activities and how you can translate your current instruction materials to utilize these available resources. At the workshop, you will set up NDP workspaces containing reproducible, executable instructional materials that educators across higher education institutions can build on and contribute to.

At the workshop

• Participants will receive detailed onboarding on how to use NDP for educational classrooms.
• Participants will participate in sharing the course they are focusing on for this workshop and the compute/materials used for classroom instruction.
• Participants will build NDP educational content as reusable workspaces.

After the workshop

• Participants will present the outcomes of their NDP educational materials in a webinar series in September.

Apply by July 5, 2026

Accepted participants in the workshop will be notified the week of July 6, 2026. Participants will receive travel coverage for flights and hotel. After the workshop, a $2,000 stipend will be issued for participation in the workshop and follow-on activities.

The HPC and Data Science Summer Institute is a comprehensive week-long workshop that covers introductory-to-intermediate topics in HPC, data science, and artificial intelligence (AI). It aims to give attendees a thorough overview of these topics to accelerate their learning process through highly interactive classes and hands-on tutorials on the Expanse supercomputer. A limited amount of travel support is available for this program.

The purpose of the Summer Institute is to give the attendees an overview of topics in High Performance Computing and Data Science and accelerate their learning process through highly interactive classes with hands-on tutorials on the Expanse Supercomputer.  Moreover, the attendees will have many opportunities to meet one-on-one with SDSC’s experts to discuss in detail the best techniques to solve their specific scientific problems. In order to benefit from the classes, the attendees are required to have familiarity with the UNIX/Linux shell. Basic programming skills (in any programming language) are strongly recommended.

Participation in the SDSC HPC and Data Science Summer Institute will be limited to 45 candidates. Applicants will be screened to make sure that they have the necessary background to benefit from the institute, and that the applicants have a compelling need for the skills being taught in their research or teaching.

Program highlights

Preparation Day (Virtual – July 23, 2026)

Orientation: Logging into the Expanse Supercomputer, Linux/Unix basics, and running supercomputing jobs.

Summer institute:

Monday & Tuesday Morning - Foundational Skills

Data Management: File systems, compression, checksums, and secure transfer tools (wget, curl).

Batch Job Workflows: Writing Slurm scripts, job scheduling, and troubleshooting submissions.

Parallel Computing Basics: Scalability principles, processes vs threads, resource allocation, and benchmarking.

High-Throughput Computing: Strategies for managing a large number of small jobs for example for large-scale parameter sweeps.

Tuesday afternoon to Friday morning - Advanced techniques:

Parallel Computing using MPI & Open MP: Learn how to leverage all cores in a machine with OpenMP and how to scale your computations across multiple nodes with MPI using C and FORTRAN.

Performance Tuning: Cache optimization, loop-level parallelization, and compiler limitations.

GPU Programming: Understand GPU architecture, learn how to program GPUs with libraries, OpenACC directives and CUDA.

Deep Learning: Covers core neural network concepts and hands-on Keras implementation, progressing to advanced architectures (e.g., deep CNNs, transfer learning) and GPU-accelerated model optimization, with applications in image/speech recognition and biomedical domains.

Python for HPC: Easily speed-up Python on a single machine with numba and then scale a fully distributed workload on a cluster with dask.


See How To Apply for further details and to apply. 

Application Process & Fees

No fee required to apply. Registration fees apply only if your application is accepted:

Academic, Student, Government, & Non-Profit Organizations: $350

Industry Participants: $600

As experimental methods continue to evolve, generating increasingly complex and high-resolution datasets, machine learning (ML) is becoming an essential tool across numerous scientific disciplines. This conference will explore emerging ML methods and their applications in scientific discovery, focusing on processing technologies and strategies to accelerate deep learning and inference.
Fee Waivers: Thanks to generous support from our sponsors, the NSF HDR Institute AI-Accelerated Algorithms for Data-Driven Discovery (A3D3) and Exploring Neural Network Processors for AI in Science and Engineering (Voyager), we are offering in-person registration fee waivers (subject to funding availability) for graduate students on a first come first serve basis. If funds are still available, we will also open up fee waivers to undergraduate students and early-career researchers (<7 years since PhD) with financial need. Please indicate in the registration form if you are seeking a registration fee waiver and do not pay the fee.
Important Deadlines

• Abstract Submission: June 1, 2026
• (Optional) Extended Abstract Submission: June 15, 2026
• Early Bird Registration: July 15, 2026
  
  Topics include, but are not limited to:
  - Machine Learning Algorithm Design & Optimization
  - Accelerated Inference & Real-Time Processing
  - Scientific Applications of Fast ML
  - Scalable & Distributed ML Systems
  - Advanced Hardware & Computing Architectures 

We welcome abstracts for:

• Presentations and/or Posters
• • New for 2026: Submitters will have the option to submit a 4-page extended abstract (paper) to OpenReview (details to follow)
• Tutorials
• Topical (birds-of-a-feather) sessions

More information and registration details will follow. We look forward to welcoming you to San Diego this September!