IBM PhD Fellowship Awards - Other Countries Grant

IBM PhD Fellowship Awards - Other Countries Grant

Status: ACTIVE
Funder: IBM Corporation
Amount: US $6,000 - US $25,000
Last Updated: January 16, 2025

Summary

The IBM PhD Fellowship Awards support outstanding PhD students globally who contribute to crucial research areas such as Semiconductor Technology, Quantum Computing, and AI. With awards ranging from $6,000 to $25,000, the program fosters innovation in technology. Nominations for the 2024 cycle are accepted from July 15 to August 30, 2024. Faculty are encouraged to nominate candidates whose research aligns with IBM's strategic goals, promoting diversity and academic excellence.

Overview

IBM PhD Fellowship Awards Since 1951, the IBM PhD Fellowship Program has collaborated with faculty, students, and universities by recognizing and supporting exceptional PhD students that address focused areas of interest in technology. The nomination period for the 2024 PhD Fellowships will be open from July 15, 2024 through August 30, 2024 11:59PM Eastern Time. Please see FAQs for additional guidelines. IBM PhD Fellowship Award Details IBM PhD Fellowships are awarded worldwide. The value of the awards vary by the country in which the university is located. Awards for other countries vary between $6,000 and $25,000.PhD Fellowship Focus Areas Current topics of interest within these areas include: Semiconductor Technology Two-Dimensional (2D) Materials Theoretical and experimental studies with atomistic simulations for discovering fundamentals and requirements for new channel materials being considered as contenders to Silicon (Si) for Complementary Metal-Oxide Semiconductor (CMOS) logic scaling aimed for future Angstrom-scale technology nodes. Growth, characterization, and processing of 2D transition metal dichalcogenides (TMD) materials for advanced CMOS logic applications. Back-End-Of-Line (BEOL) Interconnects Experimental research in synthesis and characterization of novel conductor and liner materials, nanoscale interconnect design and fabrication, interconnect performance and reliability testing. Theoretical & computational research in high-throughput discovery and screening of topological conductors for post-Copper (Cu) interconnects, including first-principles, quantum transport and machine learning methods. Quantum Computing Quantum Algorithms Theory Study of new algorithms and tools for both near-term and long-term quantum computing. This includes new techniques for getting the most out of quantum systems exploiting error mitigation or error correction, compiler research for optimizing circuits on hardware, and algorithm development for addressing problems like simulating quantum systems or quantum machine learning for fault tolerance. Quantum Algorithms Engineering Get the most out of hardware and software capabilities to scale existing quantum algorithms to regimes where classical computers struggle to perform brute-force computations. This includes identifying relevant computational problems that map efficiently to native hardware, efficient utilization of circuit transpilation, error suppression, error mitigation and quantum workflow optimization, and consistently benchmarking quantum hardware through relevant quantum computational problems. Multi-Cloud Computing and Hybrid Cloud Platforms for AI Building a Vibrant AI Hardware Ecosystem Objective benchmarking (including energy and carbon modeling and measurements), optimization, and adaptation of AI workloads on a diverse set of hardware options, including exploration of software portability layers, performance optimizations, and scalability for key AI workloads like training and inference. Tools for AI Model and Application Building Advancing the performance and efficiency of model pretraining, tuning, and inference processes and tools. Exploring best practices for application building, deployment, and management, from application patterns like Retrieval-Augmented Generation (RAG), agents, and successors, to hybrid cloud infrastructure for large-scale and performant AI systems. Expanding the Ecosystem of Open Models and Datasets Diverse model sizes and target applications, including multilingual, multimodal, and science models tackling broad societal goals like material science, drug discovery, climate change, etc., in addition to language generation. Aid AI model builders with trustworthy datasets, improved tools, and better efficiency. Security Generative AI for Security Training and fine-tuning large language models that contain the knowledge and skills necessary to reason about security, generate security content, and drive agents that automate security tasks from threat hunting, detection engineering, and incident response. These capabilities can assist and automate security practitioners and automate many security tasks. Security of Generative AI While GenAI has proven valuable in a wide range of use cases, the models and applications they drive increase the attack surface due to new threats: data and model supply chain risks (poisoning), prompt injection attacks, hallucinations and other errors, and over privileged agents provided with credentials to sensitive systems and resources. Quantum-safe cryptography Designing advanced cryptographic algorithms that resist quantum attackers; working on the standardization and integration of new quantum-safe algorithms into the existing ecosystem of cryptography; crypto agility and the development of sound methods such as cryptographic combiners to migrate to a quantum-safe future. Exploratory AI Science Mathematical Theory and Analysis of AI Architectures and Algorithms Algorithmic theory as applied to AI (probabilistic and learning) systems, including especially complexity theory, linear and multi-linear algebra, optimization, and circuit complexity. Reasoning and Planning Novel AI architectures that learn to reason and plan, and do so both in a data and an energy efficient manner. Models derived from such architectures should learn neural representations that lead to learning solutions which are demonstrably as performant, concise and interpretable as possible. They must have distinctly novel reasoning capabilities, e.g., reasoning about analogical relationships, or the ability to encode second order contextual relationships, or be able to carry out abductive reasoning. They must also be able to perform real-world, novel, reasoning tasks that have not been encountered in any data used for training. Foundation Models (FMs) and Large Language Models (LLMs) Agentic Workflows Building LLM based agents that orchestrate multi models for complex workflows, emergent behavior and learning of AI agents that collaborate with each other and/or with subject matter experts, capturing knowledge created by AI agents, develop reasoning and context-aware answering. Multi-modal foundation models Building FMs with multiple data and/or domain modalities in scientific domains encompassing physics, chemistry, materials science, life sciences, health care or climate science; determine uncertainty in multi-modal FMs, development of fusion and alignment algorithms. Trustworthy AI Trustworthy and Safe AI Clarifying the landscape and taxonomy of risks and impacts of AI safety, e.g., bias, harmful speech and actions, etc., and general trustworthiness, e.g., fitness for purpose. Building tools, methods, and benchmarks for detecting and mitigating those risks and concerns. AI Policy and Regulations Exploration of the technical implications of AI that influence government policies and regulations. This includes areas of trust and safety discussed above, as well as other social and employment impacts. 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.

Eligibility

You can learn more about this opportunity by visiting the funder's website. We invite applications for the IBM PhD Fellowship Awards from students whose graduate research work aligns with IBM’s strategic directions in Semiconductor Technology, Quantum Computing, Artificial Intelligence (AI), Multi-Cloud Computing and Hybrid Cloud Platforms for AI, Security, and Responsible Computing. Nominating faculty must submit thoroughly developed proposals, remain engaged during the vetting and due diligence process, and use the university domain email address for all correspondence.All nominees Must be nominated by a doctoral faculty member.Must be enrolled full-time in a PhD program over the academic year of the award or forfeit the fellowship. Should be within two years of their estimated graduation date at the time of the nomination.There is a limit of three nominations per department. Four nominations are allowed from each university. Faculty should coordinate their nomination(s) with their department chair. IBM encourages that Faculty will consider a diverse slate of candidates for nominating to the program.Must stay in the same program for the duration of the award —no transferring of departments or schools.Awardees will be selected based upon Their potential for research excellence. All awardees will have an IBM mentor for the duration of the award period and are strongly encouraged to intern during the year of their award. The internship is not guaranteed or mandatory. All student visas must align with terms of the internship. The degree to which the nomineesʼ research aligns with IBM focus areas. Academic standing, publications, and endorsements from faculty advisors and department heads.

Ineligibility

Students cannot nominate themselves.Nominees that are from U.S. embargoed countries or attending institutions located in U.S. embargoed countries are not eligible for the program.Receiving a comparable fellowship, internship, or support from another company or institution (except for academic scholarships) during the IBM PhD Fellowship funding period are ineligible for this award.IBM reserves the right to revoke nominee’s eligibility and withdraw the funding of the PhD award upon knowledge of such award.IBM employees, IBM supplemental employees and IBM contractors may not submit IBM PhD Fellowship nominations.IBM employees are not eligible to be nominated for an IBM PhD Fellowship.