Neuroscience and AI funded PhD

Scholarship overview

  • Degree level

    Postgraduate doctoral

  • Value

    Home level tution fees & 3.5 year UKRI level stipend

  • Number of awards


  • Academic year


  • Tuition fee status


  • Mode of study

    Full time

  • Available to

    Prospective students

  • Application deadline


  • Additional information

  • Available to applicants in the following departments

    • Computing
    • Design Engineering
    • Electrical and Electronic Engineering

Eligibility criteria

There is a requirement for a Master's level degree (at level Merit minimum) and that your undergraduate degree is at level UK 2:1, but a 1st class and/or Distinction (respectively) would be highly desirable. We welcome candidates from a broad range of disciplines, including Neuroscience, Engineering, Physics, Mathematics, Machine learning, Cognitive Science, and related fields. Applicants should have a strong analytical and computational background, with proficiency in programming languages commonly used in AI research (e.g., Python). Experience with neural network modelling, machine learning algorithms, or computational neuroscience tools is highly desirable. We are really looking for individuals who are curious, driven, and passionate about research in this exciting field. The ability to work collaboratively within a team and communicate effectively with both technical and non-technical audiences is essential.


Please note: This scholarship is not available to continuing students.

Application process

If interested, please send an email to both supervisors with a CV and a paragraph summarising your interests (we welcome research proposals if you have one, but not necessary to write one) at and

Additional information

We are seeking a PhD candidate to join us at Imperial College London. This position offers a unique opportunity to explore the cutting-edge intersection of neuroscience and artificial intelligence, with the broad goal to investigate shared principles of computation within both artificial and biological intelligent systems.

Research topics: These include, but are not limited to: (i) Information and communication within brain circuits of modular network topologies; (ii) Inductive structural and communication biases within brain-inspired neural networks; (iii) Comparing neural coding schemes; (iv) Resource rationality in task-solving neural networks and their scaling; (v) Evolutionary and developmental models of heterogeneity in the brain. Relevant papers of the supervisors include this ( and this (


If you have any additional questions, please contact us at