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 Skills 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 Skills 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
    RAD Lab

    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. 

    • Tutorial - Using the Slurm Resource Manager to host Jupyter notebooks

    • Codelab - Deploy an auto-scaling HPC cluster with slurm

    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. 

    • Codelab - Train and deploy on-device image classification with AutoML Vision

    • Google Skills - Classify images of clouds with AutoML Vision

    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: tf.data.Dataset and TFRecords 

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

    Life sciences
    Genomics

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

    • Tutorial - Genomic data processing reference architecture

    • Video - Genomic Analyses on Google Cloud

    Healthcare

    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 the 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.

    • Google Skills - Detect labels and landmarks with the Cloud Vision API

    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

    Data science

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

    • Codelab  - Run a Big Data Text Processing Pipeline in Cloud Dataflow 

    • Google Skills - Vertex AI Pipelines: Qwik Start

    Jupyter, R, and RStudio

    Perform large-scale technical computing on Google Cloud.

    • Tutorial - Data science with R on Google Cloud: Exploratory data analysis tutorial

    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.

    • Video - Google Cloud Workload Manager

    Containers and kubernetes

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

    • Tutorial - Deploying containerized workloads to Slurm on Compute Engine

    • Google Skills - Orchestrating the Cloud with Kubernetes

    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.

    • Google Skills - Dataproc: Qwik Start - Command Line

    • Codelab - Provisioning a Managed Hadoop/Spark Cluster with Cloud Dataproc

    Remote desktop and visualization

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

    • Tutorial - Setting up Chrome Remote Desktop for Linux on Compute Engine

    • Tutorial - Creating a virtual GPU-accelerated Linux workstation

    Lustre

    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

    Generative AI training

    Learn the fundamentals of Large Language Models and Google Cloud generative AI solutions.

    • Google Skills - Self-paced training on curated generative AI content

    Learn Google Cloud

    RAD Lab

    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

    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. 

    • Tutorial - Using the Slurm Resource Manager to host Jupyter notebooks

    • Codelab - Deploy an auto-scaling HPC cluster with slurm

    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. 

    • Codelab - Train and deploy on-device image classification with AutoML Vision

    • Google Skills - Classify images of clouds with AutoML Vision

    Genomics

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

    • Tutorial - Genomic data processing reference architecture

    • Video - Genomic Analyses on Google Cloud

    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 the AI Platform for serving (prediction).

    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.

    • Google Skills - Detect labels and landmarks with the Cloud Vision API

    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

    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

    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.

    Join the online community

    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.

    Learn about the program
    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

    Read the full story

    Take the next step

    Submit a proposal to receive up to $5,000 in free Google Cloud credits for academic research.
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