IBM PhD Fellowship Awards Program 2022/23(up to USD$60000 award)

About the program

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. Applications will be taken for the 2022-2023 PhD Fellowships October 11 – October 29, 2021.

Eligibility

All nominees
– must be nominated by a doctoral faculty member; students cannot nominate themselves.
– must be enrolled full-time in a PhD program over the two consecutive academic years of the award or forfeit the fellowship.
– should have three years remaining in their program at the time of nomination so the fellowship can be applied to the last two years of study.
– that are from U.S. embargoed countries are not eligible for the program.
– that are 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
– 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 first or second year of their award. All student visas must align with terms of the internship.
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.
– the degree to which the nominees’ research aligns with IBM focus areas.
– academic standing, publications, and endorsements from faculty advisors and department heads receiving a comparable fellowship, internship, or support from another company or institution (excepting in the case of academic scholarships)
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.

IBM PhD Fellowship Award awardees

For 70 years IBM has recognized and rewarded outstanding PhD students around the world through a highly competitive IBM PhD Fellowship Award program. The distinguished 2021 IBM PhD Fellowship Award recipients demonstrated expertise in pioneering research areas, such as artificial intelligence, hybrid cloud technology, quantum computing, data science, security, and the next generation of cutting-edge processors.

The 2021 IBM PhD Fellowship Award Program received hundreds of applications from 183 universities in 32 countries. Applications were reviewed by eminent technologists from across IBM. The award recipients demonstrated academic excellence as well as provided innovative, exceptional research proposals.

   Submitted applications should concentrate primarily in one of the IBM Focus Areas categories shown below.

Hybrid Cloud

• Optimization of open standards and open source code resulting in the enablement and creation of a seamless hybrid cloud platform that can be deployed anywhere. Proposals can include Platform Enablement and Optimization with an open source component; or use this category for interdisciplinary entries.
• Flexibility and Scalability. Submittals could propose research that leads to the creation of hybrid cloud platforms enables more flexible, scalable computing, unifying local environments with a virtually limitless pool of computing power and capabilities, making bits, neurons, and qubits available on-demand.
• Accelerating adoption. Making hybrid cloud adoption easier and safer by enhancing agility through automation. Submittals could address leveraging AI for code to help automate essential tasks like application modernization, vulnerability detection in code, troubleshooting of IT reliability issues, and research focusing on cutting edge technologies in antivirus and other protection mechanisms.

Artificial Intelligence

AI advancements in hardware or training models, characterizing and classifying unknown instances, and federated learning. Security topics are highly encouraged: data encryption, storage advancements, unified endpoint management, and firmware or chip-level proposals.

AI Hardware

• Processing efficiency. The next improvements in devices, architectures, and algorithms Nominations could include research that combines these topics and apply them to Deep Neural Networks far beyond the present architectures of GPUs and CMOS Accelerators.
• Digital AI Cores. New accelerators for existing semiconductor technologies that use reduced precision to speed computation and decrease power consumption using reduced precision techniques.
• Analog Cores. Memory-based technology to advance AI at VLSI, analog memory devices and hardware accelerators, mixed precision in-memory computing, hybrid design for AI Software, and other 8-bit breakthroughs.
  Hetergeneous Integration. AI applications drive the need for a system level optimization of AI Hardware through Heterogeneous Integration of Accelerators, Memory and CPU to enable high-speed/high-bandwidth connectivity components. Proposals can bridge these areas.
• Machine Intelligence/Neural Networks. Machine Intelligence differs from machine learning. Solving some of AI’s greatest challenges using associative reasoning to mimic human intelligence. with brain science.

AI Engineering

• Optimization. Tools for AI creators to reduce the time they spend training, maintaining, and updating their models. New approaches, strategies, and research to explore advanced problems automatically. Best models for ML and data science pipelines, best architectures for deep learning, and best hyperparameters for AI models and algorithms.
• Privacy and Security. IT Infrastructure consumption models, privacy assurance, hybrid cloud strategies, storage infrastructures, privacy and security assurance held in the hardware, and progressive hybrid cloud infrastructure including storage optimization.

Neuro Symbolic AI

• Deep Learning to combine the power of neural networks with symbolic methods to advance AI reasoning effectiveness.
• Neuro-symbolic AI NLP and QA. Submittals that cover applied challenges posited by neural networks, like symbolic AI, Q&A, probabilistic physics inference models, or new neuro-symbolic technique. New systems for knowledge-based question answering.

Secure, Trusted AI

• Building evaluating, and monitoring for trust. AI is developing diverse approaches for how to achieve fairness, robustness, explainability, accountability, value alignment, and how to integrate them throughout the entire lifecycle of an AI application.
• Techniques to detect and mitigate bias in datasets and models. Addressing the need for understanding and removing gender stereotypes, as well as citing and/or rating AI services for bias.
• Robustness, and Privacy. Evaluating and defending machine learning models and applications against adversarial threats and/or conform to required privacy.
• Explain-ability, Accountability, and/or Transparency. Advancing an AI system to ‘explain itself.’ Exploring the inner workings of an algorithm to provide stakeholders explanations for different purposes and objectives that are tailored to their needs.

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.

Quantum Computing

• Advanced foundational quantum information science. Exploring and developing new quantum algorithms to reduce error rates and ensure more accurate and reliable results.
• Quantum hardware. Specialized quantum hardware and systems to scale Quantum volume while also increasing qubit count.
• Quantum circuits and software to explore and develop compelling use cases for this powerful form of computing.

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