Google PhD Fellowships

Computer architecture is foundational for computer science generally, and Google in particular. Google's architecture research is diverse and deep, from processor design to understanding how AI can profoundly change the way we think about how we design circuits. We are interested in proposals in all areas of computer architecture that demonstrate creativity, deep understanding, and bold thinking.

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. 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 this area, we call for proposals specifically in the areas of:

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

Subtopics: Population health, Complex medical data, Consumer health

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 this 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

Subtopics: Human-AI Collaboration, AI for Accessibility, Responsible AI in HCI, Interactive Machine Learning, Extended Reality (XR).

The Human-Computer Interaction (HCI) research area aims to support 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.
• Extended Reality (XR): Novel perceptual algorithms and interactive systems for spatial and intelligent interaction in extended reality.

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, extended reality (XR), cross-device interaction, social computing, and interactive visualization.

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 this 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

Subtopics: Audio / Image / Video Understanding, Action Recognition, Digital Media Processing, Neural and Classical Image/Video Compression, Object Detection and Recognition, Speech, Robotics

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. A main focus is on generative methods for creating exciting and novel images and video.

Subtopics: Knowledge Retrieval & Use, Grounding & Factuality, Agentic Workflows and Tool Use, Conversational Agents, Translation & Multilinguality, Multimodality & Language Grounding to Vision, Evaluation & Data, Novel Applications of LLMs.

Google research in Natural Language Processing 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 efforts 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.”

Google Privacy, Safety, and Security is committed to ensuring that the internet is safer for everyone. To meet this 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 this 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 Google’s Quantum AI research are to develop a fault tolerant quantum computer that is capable of handling commercial, quantum-advantaged workloads 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 this area, we call for proposals specifically in the areas of:

• Quantum algorithms
• Quantum error correction
• Early fault-tolerant quantum computing
• NISQ experiments, prioritizing scientific discovery or beyond classical
• Superconducting qubits
• Classical methods for simulating quantum algorithms

Research on all aspects of software, including software engineering and programming languages. This includes software development methodologies and tools, debugging practices and tools, software testing strategies and tools, cooperation strategies for developers, interface and library design, programming language implementation, code optimization and verification techniques, etc.

Research for Systems, Networking and Cloud Computing focuses on exploration of future software and hardware for global distributed systems at unprecedented scale. We have interests ranging over the entire SW/HW stack: from firmware to operating system kernels to global storage to ML execution environments and cloud-scale orchestration; from novel networking hardware to communications protocols and network management, both for datacenters and global networks; and from custom processors to custom HW accelerators and their systems infrastructure.