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Is your organization, university, or nonprofit lab working on climate research? Learn more about the Google Cloud Climate Innovation Challenge.

Google Cloud for Researchers

Make tomorrow’s research breakthroughs possible. Accelerate your research with training, free credits, and resources from Google Cloud.

Researchers at USC use Google Cloud to accelerate the drug discovery process

Explore the benefits

Submit a proposal to receive up to $5,000 in free Google Cloud credits for academic research. Use Google's high performance computing capabilities. 

Get started with Google Cloud by accessing free online training through Google Cloud Skills Boost and applying for learning credits from the platform.

Connect to a community of peers doing breakthrough research. Share ideas through online communities or apply to be a Google Cloud Research Innovator.

Learn Google Cloud

Get access to the Google Cloud catalog in Google Cloud Skills Boost for hands-on practice. Apply to receive up to 200 credits. Share credits with students and track lab completion.

Research Discipline Overview Training
Research, development, and prototyping

Discover RAD Lab, a Google Cloud-based sandbox environment to help teams advance quickly from research and development to production. 

  • GitHub - Explore Rad Lab's code repository 

High performance computing
HPC Subscription

With Google Cloud's HPC Subscription, researchers can ramp up their projects quickly, regardless of their technical expertise level—at a fixed subscription price, avoiding overage costs. 

Environmental sciences
Image processing

Using large-scale computing power to recognize patterns and "read" images is one of the foundational technologies in AI, from self-driving cars to facial recognition. 

Flower image classification using Keras on TPUs

Learn how to build, train, and tune your own convolutional neural networks from scratch with Keras and TensorFlow.

  • Codelab - Using TPU-speed data pipelines: and TFRecords 

  • Codelab - Modern convnets, squeezenet, Xception, with Keras and TPUs 

Life sciences

With Cloud Life Sciences (formerly Google Genomics), learn to process biomedical data at scale.


Cloud Healthcare API provides a managed solution for storing and accessing healthcare data in Google Cloud, providing a critical bridge between existing care systems and applications hosted on Google Cloud. 

Social sciences
Cloud AI Platform

Get hands-on practice with TensorFlow 2.x model training, both locally and on AI Platform. After training, you will learn how to deploy your model to AI Platform for serving (prediction).

Machine learning APIs

Get hands-on practice with machine learning APIs by taking labs like Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API.

Physical sciences
Image analysis and classification

The Cloud Vision API lets you understand the content of an image by encapsulating powerful machine learning models in a simple REST API. Send images to the Vision API and see it detect objects, faces, and landmarks.

Mathematical sciences
Financial services

Google Cloud machine learning techniques, especially deep learning, hold great promise for time series analysis. As time series become more dense and begin to overlap, machine learning offers a way to separate the signal from the noise.

  • Tutorial - Analyzing portfolio risk using HTCondor and Compute Engine

  • Tutorial - Analyzing Financial Time Series Using BigQuery and Cloud Datalab 

Data science

Apply sophisticated statistical and machine learning methods to real-world problems when you build on top of the Google Cloud.

Jupyter, R, and RStudio

Perform large-scale technical computing on Google Cloud.

  • Tutorial - Running R at Scale on Compute Engine

  • Tutorial - Running RStudio Server on a Cloud Dataproc Cluster

Computer science
Media and rendering

Learn how to deploy the OpenCue render management system on Linux virtual machines (VMs) using Google Cloud.

  • Tutorial - Creating a render farm in Google Cloud using OpenCue

Workload managers

Learn to optimize utilization and efficiency through workload managers that simplify cluster administration.

  • GitHub - PBS Deployment Scripts

  • GitHub - HTCondor Deployment Scripts

Containers and kubernetes

Learn to use a managed environment to focus on experiencing Kubernetes rather than setting up the underlying infrastructure.

MapReduce - Hadoop/Spark

Create Cloud Dataproc clusters quickly and resize them at any time so you don't have to worry about your data pipelines outgrowing your clusters.

Remote desktop and visualization

Learn to set up a Chrome Remote Desktop service or a virtual Linux workstation.

  • Tutorial - Setting up Chrome Remote Desktop on Compute Engine

  • Tutorial - Creating a virtual GPU-accelerated Linux workstation


Access enterprise-class DDN EXAScaler Lustre software through the Google Cloud Marketplace and an open sourced set of scripts to easily configure and deploy a Lustre storage cluster on Compute Engine.

  • Codelab - Deploy a Lustre Parallel File System on Google Cloud

  • Marketplace - DDN Cloud Edition for Lustre

Research Discipline

Join the community

All researchers who received Google Cloud credits are added to our online researcher community. Researchers can also apply for the Research Innovator program.

Researcher community

Join fellow faculty and researchers who are using Google Cloud in their labs and in their classrooms. Only researchers who have been verified and approved to receive Google Cloud credits are eligible to join. Please check your onboarding email for a link to join; or request access using your school-issued email address.

Research Innovators

Apply to join a global community of researchers driving scientific breakthroughs with Google Cloud. Research Innovators gain access to professional development and other benefits. We are not accepting applications now, but you can learn more about the program, meet the inaugural cohort, and request to be notified when applications open.

"We’re saving time and money by running Flywheel on Google Cloud, but what’s most important is the reproducibility we’re able to achieve. The ability to share our research to benefit people all over the world goes right to the heart of science for me."

Dr. Brian Wandell, Faculty Professor and Director of CNI, Stanford University

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