Scholarship overview

  • Degree level

    Postgraduate doctoral

  • Value

    Full tuition fees at the Home or Overseas rate and a stipend of £21,237 per annum

  • Number of awards

    1

  • Academic year

    2025/2026

  • Tuition fee status

    Home, Overseas

  • Mode of study

    Full time

  • Available to

    Prospective students

  • Application deadline

    01/11/2024

  • Additional information

    c.qin15@imperial.ac.uk

  • Available to applicants in the following departments

    • Electrical and Electronic Engineering

Eligibility criteria

Applicants are expected to have a First Class or Distinction Masters level degree, or equivalent, in a relevant scientific or technical discipline, such as computer science, mathematics or engineering. Applicants should also meet the minimum requirement as outlined in the guidance on qualifications. Applicants must be fluent in spoken and written English. Good team-working, observational and communication skills are essential. Experience in one or more of the following areas is desired: machine learning, deep learning, mathematical modelling, and software engineering.

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

Course specific information

Applications are invited for a PhD studentship in the field of Multimodal Representation Learning in Biological Data, which will be jointly hosted by Department of Electrical and Electronic Engineering and the College's new I-X initiative. Home and Overseas applicants are eligible for this studentship. It is especially targeted at PhD applicants with an interest in artificial intelligence and medicine. Prospective students will also join the Biomedical Image Analysis Group.

The PhD research will explore the important topics of multimodal representation learning in biological data. Particularly, the research project will focus on finding the synergy between artificial intelligence (AI) and the large-scale multimodal high-content screening data. It will create innovative multimodal representation learning techniques that are beyond supervised learning, for realizing more efficient and accurate inference of biological relationships amongst genetic and chemical perturbations. The research is at the intersection of artificial intelligence and medicine and has the potential to make a widespread impact on the future of AI-enabled drug discovery and ultimately bring significant benefits to the pharmacy industry.

Department of Electrical and Electronic Engineering has a long and proud history of world-class research and innovation and is at the forefront of tackling the most urgent global challenges in energy, healthcare, smart technology, and communications. It ranked the 1st in the UK (Engineering) in REF 2021 based on the proportion of world-leading research (4*).

I-X is a new collaborative environment for research, education, and entrepreneurship across the areas of artificial intelligence, machine learning, data science, statistics, and digital technologies. The goal of I-X is to realise new models for research, education, and entrepreneurship that go beyond traditional siloes imposed by academic disciplines, thus forming a blueprint for the university of the future. I-X benefits from a strategic investment by the College, which includes new facilities on Imperial’s White City and South Kensington campuses. For more information about Imperial-X, please visit the I-X website.

Application process

To Apply, please choose Electrical and Electronic Engineering Research Program and Intelligent Systems and Networks Group, then indicate Dr Chen Qin as a potential supervisor when making the application. Early applications are encouraged. The recruitment is on a rolling basis. The post is preferred for candidates who can start in January or April 2025. For further details of the post, please contact Dr Chen Qin at c.qin15@imperial.ac.uk. For queries regarding the application process, please contact eeepgadmissions@imperial.ac.uk.

Contact

If you have any additional questions, please contact us at c.qin15@imperial.ac.uk.