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Internship: Privacy-Preserving AI in Decentralized Personal Data Architectures

Posted: 27th March 2023

Internship: Privacy-Preserving AI in Decentralized Personal Data Architectures

The Human Centred Computing (HCC) Research Group ( ) in Oxford Computer Science Department is excited to announce three summer internships in 2023!!!

Please join us if you want to develop privacy-friendly AI, with a group of world-leading computer scientists!

Detailed job description

You will be working as part of the Oxford Martin School programme EWADA (Ethical Web and Data Infrastructure in the Age of AI) [1]. Data driven algorithms are positively changing every walk of our life. However, from simple data aggregation algorithms for drawing collective insights to more advanced machine learning algorithms, all involve computations that are currently performed using centralised access to the users' data. During the internship, you will be responsible for building scalable systems to perform privacy-preserving artificial intelligence (AI) computations in decentralized personal data architectures to contribute to the creation of a more ethical AI ecosystem. Specifically, you will use the Solid (Social Linked Data) architecture [2], upon which to build such AI systems and algorithms. Interns will demonstrate the practical significance of their work in application use-cases and we have a range of application use-cases for the internship, including but not limited to:

  • Personalised Recommender Systems
  • Large Language Models like GPT
  • Open (Health) Data
  • Algorithmic Fairness and Transparency

Background of the project

EWADA is an ambitious 3-year programme that aims to reform the concentration of power on the Web by developing and deploying new forms of technical and legal infrastructure. The project is led by Prof Sir Nigel Shadbolt and Prof Sir Tim Berner-Lee and aims to investigate novel re-decentralisation architectures and develop privacy-preserving AI methods to re-establish citizens’ self-autonomy on the Web.

Selection criteria

You must have hands-on programming experience with machine learning, strong problem solving skills and a demonstrated passion for building large-scale systems and performing comprehensive empirical evaluation. You either have prior experience or are interested and willing to learn quickly about privacy-preserving techniques like multi-party computation and Solid ecosystem. Successful candidates are also expected to be able to work independently.


The post is expected to be full-time (36.5 hours) for 12 weeks, starting mid-July 2023 and ending in September 2023, £14.09 - £15.66 (Grade 3.8 - 4.7) per hour, depending on experience. If you are a student holding a Tier 4 visa, then you are permitted to work full-time for 8 weeks, plus 4 weeks part-time (max 20 hrs per week).

The post does not have to be based in Oxford but will be subject to the right to work in the UK. We CAN NOT sponsor visa applications due to the short duration of the project.

Applications should be submitted to Human Resources Department at  with a resume or CV. A short paragraph on your background, interests and motivation to apply will be helpful.

The subject of the email should be: “Internship Application for Privacy-Preserving AI in Decentralized Personal Data Architectures”

The closing date for applications is noon on Friday 16th June 2023.  Candidates will be shortlisted and invited for an interview in late June.

Selection criteria


  • Fundamental understanding and hands-on experience with implementing machine learning.
  • Proficiency in Python and ability to work with Linux-based Operating Systems.
  • The ability and desire to learn about Solid, to quickly acquire domain expertise needed for effectively developing new systems on top of Solid.
  • The ability to communicate information clearly, including technical content.
  • The ability to work independently and think creatively.
  • The ability to effectively manage time, to complete projects efficiently.


  • Experience with deep learning.
  • Experience in privacy-friendly techniques like multi-party computation, homomorphic encryption, differential privacy, federated learning etc.
  • Experience with distributed and decentralized systems.
  • Familiarity with basic cryptographic techniques.
  • Excellent writing and presentation skills.