Research scholar program

Algorithms and optimization form the foundations of computer science, focusing on designing efficient methods to solve complex contemporary problems including problems with applications in machine learning, data science, and modern AI. The primary goals in this area are to create methods that improve resource efficiency and sometimes offer guarantees on the quality of the solution. Key focus areas include combinatorial optimization, market algorithms, operations research, continuous optimization and learning, Scalable algorithms, and general topics in theoretical computer science. This line of research is crucial since it studies the solvability of problems through a set of tools that nicely complement machine learning techniques.

For our area, we call for proposals specifically in the areas of:

• Combinatorial optimization
• Market algorithms
• Operations research
• Continuous optimization and learning
• Scalable algorithms
• Other

Large language, visual, and multimodal models have made significant advances in recent years, opening up new possibilities for scientific research. The program will evaluate proposals based on their scientific merit, creativity, and potential impact on the field of scientific research.

For our area, we call for proposals specifically in the areas of:

• Applications: Proposals that demonstrate how large language models can be used to advance scientific discovery in a specific field.

• Foundations and agentic tasks: 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 agent based models that address complex multi-step tasks potentially requiring code generation, developments of models that help in synthesis of scientific literature, and accelerating scientific analysis, experimentation, and summarization with or without humans in the loop.

• Evaluation: Proposals that develop datasets or methods for benchmarking and evaluating large models for science, including evaluation of coding ability using appropriate libraries, 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.

Google’s Health research aims to advance AI and technology that helps people live healthier lives. Achieving this goal will require collaborative research with public officials, clinicians, and consumers. In partnership with public officials, we are creating tools to understand population level health. With clinicians, we are developing novel algorithms to better understand and make use of complex medical data such as images, text, lab tests, and genomics. With consumers, we are developing technology that helps people find high quality health information and better understand their own health status. By focusing on inclusive, transformative research we aim to improve the lives of billions of people.

For our area, we call for proposals specifically in the areas of:

• Generating and understanding large datasets of the world to derive useful insights for improving population health, especially in under resourced regions or communities

• Novel algorithm development for better understanding of complex medical data, with focus areas in novel methods, novel applications, or underserved settings

• Novel methods, including both software and hardware, that helps extract health insights cheaper, faster, or better

The Google Research Scholar Program in Human-Computer Interaction (HCI) supports academic research advancing innovative, human-centered interactive systems. We are particularly interested in proposals exploring foundational principles, guidelines, and theories shaping the future of HCI in the era of generative AI, including but not limited to:

• Human-AI Collaboration: Novel interaction paradigms, explainable AI, and trust in AI systems.
• AI for Accessibility: Leveraging AI to make technology more inclusive.
• Responsible AI in HCI: Ethical, fair AI systems that respect user privacy and agency.
• Interactive Machine Learning: Enabling users to understand, control, and interact with ML models.

While we welcome research across all HCI sub-areas, we are particularly excited about proposals aligned with Google's focus on predictive and intelligent UIs, mobile and ubiquitous computing, social computing, and interactive visualization.

This program offers researchers the opportunity to collaborate with Google researchers and engineers and contribute to shaping the future of HCI.

Machine learning, a cornerstone of Google's research initiatives, encompasses a vast spectrum of exploration. This includes fundamental theoretical investigations into algorithms and their underlying principles, as well as the development of practical applications that address real-world challenges.

Through these diverse research endeavors, Google aims to advance the state-of-the-art in machine learning and harness its potential to drive innovation across a wide range of domains.

For our area, we call for proposals specifically in the areas of:

• Learning algorithms & techniques
• Learning theory
• Federated learning
• Information theory
• Optimization for ML algorithms
• Reinforcement learning
• Robotics
• Recommender systems

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.

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.”

Networking is central to modern computing, from WANs connecting cell phones to massive data stores, to the data-center interconnects that deliver seamless storage and fine-grained distributed computing. Because our distributed computing infrastructure is a key differentiator for the company, Google has long focused on building network infrastructure to support our scale, availability, and performance needs, and to apply our expertise and infrastructure to solve similar problems for Cloud customers. Our research combines building and deploying novel networking systems at unprecedented scale, with recent work focusing on fundamental questions around data center architecture, cloud virtual networking, and wide-area network interconnects. We helped pioneer the use of Software Defined Networking, the application of ML to networking, and the development of large-scale management infrastructure including telemetry systems. We are also addressing congestion control and bandwidth management, capacity planning, and designing networks to meet traffic demands. We build cross-layer systems to ensure high network availability and reliability. By publishing our findings at premier research venues, we continue to engage both academic and industrial partners to further the state of the art in networked systems.

For our area, we call for proposals specifically in the areas of:

• Cloud Networking: We constantly evolve cloud networking solutions to provide a great cloud experience to billions of users. Our focus area covers customer-facing networking API design to the network data and control plane programming including HW programming. We exercise the Hybrid Research model by deploying our solutions in the Google Cloud Platform, which is one of the largest and fastest-growing cloud providers in industry. Our activities include networking, distributed systems, network security, kernel programming and algorithms.

• WAN networking (a.k.a. GGN) is responsible for the design, development, build and operation of Google’s global network that every Google service runs on. GGN develops cutting-edge networking technologies that allow Google's global WAN to be zero touch, builds out some of the largest scale Software Defined Networks (SDNs) infrastructure ever deployed, develops sophisticated software systems for network capacity forecasting, planning and optimization, designs and implements new optical technologies. GGN relies on the most advanced techniques in network hardware and software, traffic engineering, and network management to deliver unprecedented scale, availability and performance at industry leading cost points. Additionally, we are also advancing the state of the art in data analytics and machine learning to drive network efficiency and optimization at scale.

• Network Infrastructure for Data Centers brings together networking, distributed systems, kernel and systems programming, end-host stacks, and advanced algorithms to create the datacenter networks that power Google. We deploy real-world systems at a global scale.

Google Privacy, Safety, and Security is committed to ensuring that the internet is safer for everyone. To meet our goal, we support and partner with academia to bring about state of the art advancements across a broad range of privacy, security, and safety areas.

For our area, we call for proposals specifically on:

• Novel applications of AI for privacy, security, and safety

• Ensuring the privacy, security, and safety of AI systems

• User and measurement studies of privacy, security, and safety

• Applied cryptography

• Differential privacy

• Hardware security and side-channel analysis

• Software vulnerabilities, software supply chains, and fuzzing

Topics outside of these areas will still be considered. However, we encourage applicants to align their proposals with one of the above topics.

Two primary goals of the Quantum AI team are to develop a functional quantum computer that can tolerate errors and to identify novel applications that can be executed on quantum hardware. We actively collaborate with academic partners to advance these goals and we welcome the submission of proposals containing innovative ideas.

For our area, we call for proposals specifically in the areas of:

• Quantum algorithms
• Quantum machine learning
• Quantum error correction
• Early fault-tolerant quantum computing
• NISQ experiments
• Superconducting qubits
• Neutral atom quantum computing

Research on all aspects of software development, including the engineers and the programming languages, libraries, development tools, and processes that they use. This includes software development methodologies and tools, debugging practices and tools, software testing strategies and tools, cooperation strategies for developers, interface and library design, code optimization and verification techniques, etc.

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.

Applications are open to academic researchers who are currently advising students and conducting research in technology and computing at institutions. Applicants must be a full-time assistant, associate, or professor at a university or degree-granting research institution and have received their PhD within seven years at the time of the application submission.

Institutions:

• We accept applications from full-time faculty at universities around the world. Funding is focused on supporting the faculty’s research. We do not allow applications from non-degree-granting research institutions.
• Since our funding is structured as unrestricted gifts to degree-granting Universities, we cannot process awards to other institutions (e.g. not-for-profits institutions, hospitals, non-degree-granting research institutes, etc) even if they are affiliated with a University. A Principal Investigator must apply in his or her capacity as a university professor and must be able to accept an award through that University.

Principal Investigator Requirements:

• Global faculty who have received their PhD less than 7 years from submission from degree-granting institutions who are doing research within fields relevant to Google.
• An applicant may only serve as Principal Investigator or co-Principal Investigator on one proposal per round, they cannot be listed on two separate proposals.
• We understand that titles may differ globally. In order for someone without the title of professor to apply, he or she must be a full-time faculty member at an eligible institution and serve as a formal advisor to masters or PhD students. We may, at our discretion, provide funding for Principal Investigators who advise undergraduate students at colleges that do not award advanced degrees.

Past Applicants:

• If an applicant’s proposal was not selected for funding the previous round, they are welcome to apply with a new proposal (or substantively revised proposal) the following round. A Principal Investigator can apply a maximum of 3 times within the 7 years post-PhD.

Our Application Companion supports applicants with structuring and developing high-quality applications. It includes the program overview, how to apply and formatting guidelines, samples of high-scoring responses, open advice to proposers and a submission FAQ.

Filename:

Submit your proposal and CV as a single PDF file. Name the file in the following format: "[First InitialLast name]-2025" (e.g., "JDoe-2025"). Use only letters, numbers, and hyphens.

Proposal Length:

• 5 pages maximum for a single Principal Investigator (PI) submission
• 7 pages maximum for submissions with a co-PI with CV

Formatting:

• Single-spaced
• 1-inch margins
• Times New Roman 12-point font

Proposal should include the following numbered sections:

Overview (3 pages max):

1. Proposal Title
2. Full name, contact information (postal address, email address, phone), and affiliation (institution and/or department) of PI(s)
3. Abstract (concise summary of proposal)
4. Research goals and problem statement
5. Description of the proposed work, expected outcomes, and results
6. Discussion of how the research relates to prior work (including your own)
7. Explanation of your qualifications to conduct this research
8. For ongoing projects, explain how this funding would enhance your existing project.
9. Data policy: Describe your intentions for sharing the project's output with the broader research community (e.g., open-sourcing code, making datasets public). Please note that for those awards that are structured as unrestricted gifts, there are no legal requirements once a project is selected for funding. This is simply a statement of your current intentions. However, for research area topics that are not awarded as unrestricted gifts (usually those that require the use of a specific product, methodology, or other constraint), open sourcing the software, models, or other intellectual property developed during the project will be a mandatory condition for receiving the award, unless otherwise specified in a separate agreement between Google and the recipient.

CVs (4 pages max):

1. Primary PI: 2-page max CV required
2. Co-PI: 2-page max CV (optional)

Important notes

1. The co-PI's CV is the only content allowed on the additional 2 pages of a co-PI proposal. Any submitted CV longer than 2 pages will be truncated before review.
2. Proposals without a co-PI's CV should not exceed 5 pages.
3. References should be excluded from the proposal itself. Instead, use the designated sections in the application form for this information.

Please do not include budget details in your proposal. We will be providing flat funding amounts based on the cost of student tuition on a regional basis.

Co-PIs must generally meet the same eligibility criteria as primary PIs, except in cases where the co-PI is a postdoctoral researcher.

While we are likely to fund submissions that align with the listed research areas and subtopics, we are open to innovative proposals within these areas.

Here's how to strengthen your proposal:

1. Clearly define the problem. Good research starts with a compelling question.
2. Describe a specific, achievable outcome. What will this research enable that wouldn't happen otherwise, and how? Outline both minimum expected and best-case scenarios, specifying the datasets and test cases you'll use.
3. Differentiate your contribution. Clearly explain how your work advances the state of the art, using citations and other standard practices.
4. Outline your approach. Explain your plan for addressing the research challenges, even if all answers aren't yet known. Identify potential risks and mitigation strategies.
5. Contextualize the work. Describe existing funding and how this proposal fits into your broader research goals. How will this research be used? Will it build research capability, create a tool, reproduce a result, foster collaboration, follow up on an idea, or explore a new one? We are interested in all possibilities.
6. Make it accessible to non-experts. While we try to have your proposal reviewed by a Google expert in your field, it will also be read by non-experts, so please ensure the motivation and outcomes are understandable to a broad audience.

Your proposal should ultimately demonstrate how your research aligns with our mission to recognize and support academic researchers whose work in computing and technology makes a positive difference in the world.

We completely understand the desire to receive feedback and do our best to meet this request. However, due to the high volume of applications received, you may not receive feedback on your proposal.

December/January: Applications open

February-May: Proposals are under review

June: Applicants are notified of decision

Only complete applications that meet the following criteria will be scored:

• Submitted by eligible applicants
• Related to computing or technology
• Adhere to the required formatting guidelines

Scoring will be based on the following areas:

• Faculty merit: Faculty is accomplished in research, community engagement, and open source contributions, with potential to contribute to responsible innovation.
• Research merit: Faculty's proposed research is aligned with Google Research interests, innovative, and likely to have a significant impact on the field.
• Proposal quality: The research proposal is clear, focused, and well-organized, and it demonstrates the team's ability to successfully execute the research and achieve a significant impact.
• AI Ethics principles: The research proposal strongly aligns with Google's AI Principles
• Broadening Participation: The proposal demonstrates a strong commitment to broadening participation in computing through initiatives such as mentoring underrepresented groups, establishing collaborations with diverse institutions, and disseminating research in accessible formats.
• For research area topics that require the use of a specific product, methodology, or other constraint, we will evaluate your project based on how well it adheres to and utilizes these aforementioned factors, as well as the overall quality of your approach.

Funding awards made in any form should not be used for overhead or indirect costs.

Faculty members are eligible to receive the Research Scholar award only once.

Please reach out to research-awards@google.com with any questions or concerns, and our team will be happy to assist you.