AIBLOCKSYS: A Blockchain-Based Framework for Tracking Ownership, Versioning, and Transactions of Artificial Intelligence Models
Doctoral Training Grant Funding Information
This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.
How to Apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form.
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.
- Upload two references to your online application form (at least one of which must be an academic reference).
- Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
- International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores.
Frequently Asked Questions
To help support you to complete your application, please consult the frequently asked questions below:
Project title: A Blockchain-Based Framework for Tracking Ownership, Versioning, and Transactions of Artificial Intelligence Models
Project Lead: Sabreen Ahmadjee
Project ID: 12 - 46493819
Project description:
This research project proposes the development of a blockchain-based framework for the secure, transparent, and decentralised management of artificial intelligence (AI) models. As AI models become critical digital assets across sectors, from healthcare and finance to smart infrastructure, the need for robust mechanisms to manage their ownership, versioning, and transactional integrity has become increasingly urgent. However, existing centralised solutions are inadequate for ensuring the transparency, auditability, and security required for such valuable and sensitive resources.
The core objective of this project is to adapt blockchain technology, specifically its mechanisms of decentralisation, immutability, and programmable trust, to create a system that can register, track, and govern the lifecycle of AI models. Drawing inspiration from non-fungible tokens (NFTs) and their underlying standards such as ERC-721 and ERC-1155, this research will explore how AI models can be uniquely represented on the blockchain, complete with cryptographic identifiers and linked metadata. This metadata will detail the model’s architecture, performance metrics, provenance, and licensing terms.
A key contribution of this research will be the development of methods for tracking model versioning and lineage. As AI models evolve through fine-tuning and retraining, maintaining a transparent chain of modifications and derivations is critical for reproducibility and accountability. The blockchain’s ledger will serve as an immutable record, while off-chain decentralised storage (e.g., IPFS or Arweave) will be leveraged to handle the large file sizes of AI models efficiently. A blockchain oracle will be used to detect any changes or updates to the AI model and automatically update the on-chain metadata, ensuring consistency between the model and its blockchain representation.
Beyond static ownership, the research will investigate transactional functionalities such as lending, licensing, and royalty-based resale using smart contracts. This opens opportunities for monetising AI models while ensuring that original creators receive fair compensation, addressing an emerging need in AI research and innovation ecosystems. A conceptual ontology will be developed to define and model the relationships between key entities in the ecosystem, such as owners, users, licences, and computational resources, enabling semantic interoperability and system design formalisation.
An innovative aspect of this project is its extension into federated learning environments, where training data remains decentralised. Blockchain will be applied to record contributions, model updates, and reward distributions among participants, preserving data privacy while maintaining transparency and traceability. Smart contracts will orchestrate key stages of federated learning, including participant registration, model aggregation, and incentive mechanisms.
The rationale for this research lies in the convergence of two technological frontiers: blockchain and AI. By integrating these domains, the project seeks to overcome current limitations in AI model governance and catalyse new models of ownership, sharing, and collaboration in AI development. With growing regulatory attention to digital assets and AI accountability, this research aligns with societal needs for ethical, transparent, and secure digital infrastructure.
In summary, the proposed project aims to contribute a novel technical and conceptual framework for AI model management via blockchain. Its outcomes will be valuable not only for researchers and developers but also for policymakers and organisations seeking to participate in a secure and decentralised AI economy.
Anticipated findings and contributions to knowledge:
This research is expected to produce a novel framework for managing artificial intelligence (AI) models using blockchain technology. The findings will demonstrate how decentralised, tamper-proof systems can be applied to track AI model ownership, control versioning, and facilitate secure transactions such as licensing, lending, and resale. A key contribution will be the adaptation of non-fungible token (NFT) standards (e.g., ERC-721, ERC-1155) to represent unique AI models on-chain, along with the development of a metadata structure that ensures transparency and auditability of model details. The project will also introduce a mechanism, powered by blockchain oracles, that automatically detects updates or modifications to AI models and reflects them in the on-chain metadata. This dynamic linking between the model and its blockchain record will provide a robust method for ensuring consistency, provenance, and trustworthiness. Another major outcome will be a formalised ontology capturing the relationships between stakeholders (owners, users, contributors) and entities (models, licences, transactions), which can support system interoperability and guide future regulatory or standardisation efforts. Furthermore, the integration of this system with federated learning environments will contribute new knowledge on how decentralised AI training can benefit from blockchain-based governance and reward mechanisms. Altogether, the research will advance the understanding of how AI and blockchain can converge to solve real-world problems in data ownership, model accountability, and decentralised innovation. The proposed framework is expected to serve as a foundational reference for future systems aiming to support fair, secure, and transparent AI ecosystems.
Person Specification:
Entry Requirements:
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To apply for our Computing PhD Research Degree you should have, or expect to be awarded, a Master’s degree in a relevant subject area from a British or overseas university.
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Exceptional candidates without a Master’s degree, but holding a first class or upper second class Bachelor’s degree in a relevant subject area, may be considered.
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We also welcome enquiries from potential PhD researchers with appropriate levels of professional experience.
Essential Criteria:
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Applicants must have a solid understanding of blockchain technology, including how smart contracts work and how to use platforms like Ethereum or Hyperledger.
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The applicant should also be familiar with machine learning processes such as model training, versioning, and deployment, and know how to use tools like MLflow or DVC to manage AI models.
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Strong programming skills, especially in Python, and experience with Git and APIs are essential.
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The candidate should understand basic cybersecurity concepts like encryption and digital signatures to ensure data security.
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Good academic writing, research skills, and the ability to work independently and as part of a team are also important.
Desirable Criteria:
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It would be beneficial if the applicant has previous research experience in AI or blockchain, or has worked on related projects.
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Experience with building decentralized applications (DApps), using tools like Truffle or Hardhat, and knowledge of blockchain scaling solutions like Layer 2 is desirable.
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An understanding of data ownership, intellectual property, or digital asset tracking.
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Familiarity with cloud platforms such as AWS or Azure, and experience in system architecture or data governance.
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A strong interest in emerging technologies and a motivation to solve real-world problems using AI and blockchain.
Overseas applicants:
International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.
Contact:
If you have any questions or need further information, please use the contact details below:
- For enquiries about the funding or project proposal, please contact: Sabreen.ahmadjee@bcu.ac.uk
- For enquiries about the application process, please contact: research.admissions@bcu.ac.uk