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PhD Studentship: Intrinsically-aligned machine learning

Oxford Brookes University - Faculty of Health, Science and Technology - School of ECM

Qualification Type: PhD
Location: Oxford
Funding for: UK Students, EU Students, International Students
Funding amount: £21,805 p.a.
Hours: Full Time
Placed On: 1st May 2026
Closes: 22nd May 2026

3 Year Full-time PhD Studentship Funded by Leverhulme Trust

Eligibility: Open to Applications from Home and International students

Bursary p.a.: £21,805

Start Date: 14th September 2026

Application deadline: 22nd May 2026

Course length (full time): 3 years

Director of Studies: Prof Fabio Cuzzolin, Dr Matthias Rolf

Entry Requirements: At least an upper second class degree (preferably MSc) in a Science or Technology discipline.

Essential Criteria:

  • Good working knowledge of machine learning and deep learning.
  • Hands-on knowledge of Python or PyTorch for implementing machine learning and/or deep learning algorithms.
  • Capability to work both independently and as part of a team.
  • Excellent written and oral communication and organisational skills. Proficiency in written English is required.
  • A real passion and commitment for research.

Desirable criteria:

  • Knowledge of a variety of deep learning architectures and methods.
  • Knowledge or past work on explainability in AI.
  • Previous publication record in relevant fields: AI, machine learning, computer vision, etc.
  • Previous successful project on a relevant topic.
  • Good knowledge of statistics, probability or statistical learning.

Project Description: Whereas traditional machine learning is solely interested in model selection (i.e., identifying, given the available data for the task at hand, the model that is expected to perform best), we propose a new paradigm for an “intrinsically-aligned” artificial intelligence, where accuracy, fairness and explainability are all taken into account when selecting the “best” AI model.

In a truly cross-disciplinary effort, this project, funded by the Leverhulme Trust and in collaboration with the University of Manchester, will leverage results from human decision-making to inform the design of this new paradigm, and feed the results of the latter back into human decision-making to help make it more explainable.

Please complete your online application via the above 'Apply' button.

Please note the following:

Title: PhD Studentship – Intrinsically-aligned machine learning

Please be advised that the selection process will involve an interview.

Select the following course: MPhil/PhD in Computing

Include the following documents:

  • Two references
  • A research proposal
  • Previous degree certificates and transcripts
  • Scan of passport
  • English language qualification (international candidates only)
  • CV
  • Cover letter

Applications must be completed by 5pm GMT on May 22, 2026.

For informal inquiries about the project or the application process contact Dr Matthias Rolf.

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