Research scholar program
The Research Scholar Program aims to support early-career professors who are pursuing research in fields relevant to Google.
The Research Scholar Program provides unrestricted gifts to support research at institutions around the world, and is focused on funding world-class research conducted by early-career professors.
Program status
Applications are currently closed.
Decisions for the November 2023 application will be announced via email by April 2024. Please check back in Fall 2024 for details on future application cycles.
Award information
We encourage submissions from professors globally who are teaching at universities and meet the eligibility requirements. It is our hope that this program will help develop collaborations with new professors and encourage the formation of long-term relationships.
Awards are disbursed as unrestricted gifts to the university and are not intended for overhead or indirect costs. They are intended for use during the academic year in which the award is provided to support the professor’s research efforts.
Eligibility criteria
- Applicants must be a full-time assistant, associate, or professor at a university or degree-granting research institution at the time of the application submission.
- Post doctoral staff can only serve as a co-PI, not a primary PI.
- Applicants must have received their PhD within seven years of submission (e.g., applicants in 2023 must have received their PhD in 2016 or later).
- We consider exceptions for applicants who have been teaching seven years or fewer and had delays, such as working in industry, parental leave, leave of absence, etc. This exception request can be documented on the application.
- Applicants can submit one application per round.
- Faculty can only serve as a PI or Co-PI per round. Applicants cannot serve on two separate proposals.
- Applicants can apply a maximum of 3 times within the 7 years post-PhD.
Funding amounts
The funds granted will be up to $60,000 USD and are intended to support the advancement of the professor’s research.
Supporting cutting-edge research
Our team conducts research in graph mining, optimization, operations research, and market algorithms to improve Google's infrastructure, machine learning, and marketplaces. We collaborate with teams across Google and perform research in related areas, such as algorithmic foundations of machine learning, distributed optimization, economics, and data mining.
Google Health research aims to advance AI and technology to help people live healthier lives through collaborative research with public officials, clinicians, and consumers. We are developing tools to understand population health, novel algorithms to better understand and use complex medical data, and technology to help people find high-quality health information and understand their health status.
We invite proposals that will generate and understand large datasets to improve population health, develop novel algorithms for better understanding of complex medical data, and develop novel methods to extract health insights cheaper, faster, or better.
Machine learning is the foundation of Google's research, with a broad scope that includes foundational and algorithmic work, critical real-world applications, and topics, such as federated learning, information retrieval, learning theory, optimization, reinforcement learning, robotics, and recommender systems.
Our team comprises multiple research groups working on a wide range of natural language understanding and generation projects. Our researchers are focused on advancing the state of the art in natural language technologies and accelerating adoption everywhere for the benefit of the user. Natural language processing and understanding plays a major role in driving Google’s company-wide OKRs as language understanding is the key to unlocking Google’s approach: “Build a more helpful Google for everyone that increases the world’s knowledge, success, health, and happiness.”
The Quantum AI team is developing an error-corrected quantum computer and discovering valuable applications by offering a quantum computing service. We collaborate with academic partners to advance both goals, so if you have a quantum algorithm you would like to run on our service, please submit a proposal.
Research on all aspects of software development, including the engineers and the programming languages, libraries, development tools, and processes that they use.
Large language, visual, and multimodal models have made significant advances in recent years, opening up new possibilities for scientific research. We invite proposals in these four areas:
- Applications: Proposals that demonstrate how large language models can be used to advance scientific discovery in a specific field.
- Foundations: Proposals that explore broad advances in building, tuning, or deploying large models for scientific research, such as integrating language models with specialized scientific tools, developing multimodal models for understanding scientific data, and accelerating scientific analysis, experimentation, and summarization.
- Evaluation: Proposals that develop datasets or methods for benchmarking and evaluating large models for science, including evaluating domain-specific knowledge, assessing factuality and grounding, evaluating multimodal capabilities, and developing tasks that require multi-step scientific reasoning.
- HCI: Proposals that enhance scientific workflows, such as automating complex simulation pipelines, with large language models and human-in-the-loop interaction.
HCI researchers at Google design and build large-scale interactive systems that aim to be humane, simple-to-understand, and delightful to use. We work across a variety of HCI disciplines, including predictive and intelligent user interfaces, mobile and ubiquitous computing, social and collaborative computing, and interactive visualization.
Machine perception researchers at Google develop algorithms and systems to tackle a wide range of tasks, including action recognition, object recognition and detection, hand-writing recognition, audio understanding, perceptual similarity measures, and image and video compression.
Google's privacy research reaches across multiple teams, focusing on different aspects of privacy to advance the state of the art and develop tools to protect users and give them control over their data. This includes work on privacy-preserving technologies using cryptography and differential privacy, machine learning for privacy, user interface design and human-computer interactions to make communication clear and empower users, privacy policy to define Google's guiding principles for user protection, and system analysis and measurement to develop techniques to evaluate the privacy health of Google's systems.
Google's security and anti-abuse research team brings together experts from multiple disciplines to defend users from a wide range of threats. This includes work on access control, information security, networking, operating systems, language design, cryptography, fraud detection, machine learning for abuse detection, denial of service, emerging threats, user interfaces, and other human-centered aspects of security.
Google's systems and networking systems research is focused on building and deploying novel systems at unprecedented scale. Our work spans the entire spectrum of computing, from large-scale distributed systems to individual machines to accelerator technologies.
We address fundamental questions around data center architecture, cloud virtual networking, wide-area network interconnects, software-defined networking, machine learning for networking, large-scale management infrastructure, congestion control, bandwidth management, capacity planning, and designing networks to meet traffic demands.