The nomination window for the 2023 PhD Fellowships is closed. Check back on this page periodically for announcement of the 2024 PhD Fellowships.
Hybrid Cloud
Platform and Developer Productivity for the Hybrid Cloud: includes (i) middleware to support data and compute-intensive AI/HPC workflows; (ii) the application of Machine Learning to cloud development to improve correctness, security, and performance; and (iii) software for multi-cluster/multi-cloud management.
Next generation cloud infrastructure technologies: for (i) compute, such as exploitation of novel accelerators, co-design of systems for AI, and integration in container environments; (ii) storage, such as increasing density of tape and flash-based enterprise storage systems, exploring computational storage, and automating resiliency and cybersecurity protection; (iii) networking, such as multi-cluster and multi-cloud deployments, application-centric networking and leveraging Linux kernel mechanisms for secure and efficient networking data plane
Advanced CMOS and packaging technologies: Advanced CMOS technologies, includes (i) materials and processes for semiconductor technology scaling, (ii) advanced nanosheet, vertical, and stacked transistor architecture design and integration, (iii) Cu & post-Cu interconnects. Advanced packaging technologies, includes (i) organic laminates & high- density chiplet integration, (ii) silicon bridge heterogeneous integration, (iii) wafer-level fan- out, (iv) 3D integration of compute core and memory / Cu hybrid bonding.
Artificial Intelligence
Foundation Models and Self-Supervised Learning Approaches: Includes software stack for FM training and inference, self-supervision, large-scale transformer-based models; building specific foundation models; developing toolchains for consuming foundation models; next- generation of architectures and methods to evolve foundation models for the future.
Human-centered AI: Includes human-AI collaboration and co-creation; responsible and human-compatible AI; natural language interaction.
Measuring AI: Novel AI Metrics enabling assessment of dimensions not currently measured; novel metrics measuring aspects of the human experience and performance of working and co-creating with AI; measuring all aspects of human-AI augmentation currently not addressed.
Generative AI: Includes generative AI surrogate models operating under constraints (physics, materials cost, fabrication tolerance, etc); novel methods for training AI surrogate models for simulations; improvements in data efficiency and privacy such as active learning and synthetic data.
Security
Designing for security and compliance in niche areas or across the stack: from the hardware, encryption technologies, the hybrid cloud platform, to the SecDevOps pipeline. Trusted service identity/ identity access management across the stack or address niche areas such as high assurance through Encrypted Container Images. Research covering any and all arrays of software and platforms are encouraged.
Use of Advanced Cryptographic Techniques: Includes invention, standardization and deployment of quantum-safe cryptographic algorithms; crypto agility; methodologies and tools for migrating to a quantum-safe future; ability to compute and run applications on encrypted data using capabilities such as fully homomorphic encryption and secure multi- party computation with associated key management techniques.
Decentralized Trust Technologies: Includes mechanisms for enabling and deploying self- sovereign identity; central bank digital currencies technologies; interoperability of blockchain technologies; software and hardware supply chain security.
Quantum Computing
Hardware: Includes superconducting qubits, quantum control, firmware, cryoelectronic circuit design, quantum microwave engineering, and quantum FPGA engineering.
Theory & Applications: Includes quantum error correction, quantum complexity theory, quantum simulations, and quantum algorithms.
Software: Includes quantum machine learning, quantum biology and health informatics, quantum optimization, quantum software architecture, quantum compilers and optimizers, and hybrid cloud.
Responsible and Inclusive Technology
Responsible Computing: Research at the intersections between ethical, legal, social, historical, cultural, epistemological, and technical aspects of computation, including the datasets, technologies, processes, and infrastructures involved; implications of advanced and emerging technologies – e.g., cloud computing, AI & machine learning, quantum computing - or technologies that interact with these capabilities; creation of sociotechnical strategies that constructively challenge and mitigate potentially harmful outcomes and create tangible places for collaborative and critical computational praxis.