This studentship offers a chance to go deep in the areas comprising machine learning, statistical learning, and the acquisition of managerial skills to develop, manage, and communicate the research findings.
- Qualification Type: PhD
- Hours: Full Time
- Title of project: Improving Predictive Models with Causal Structure Learning
- Placed On: 1st February 2022
- Closes: 15th May 2022, or until places have been filled.
Applications are invited for a PhD studentship in the Department of Computer Science. The successful candidate will have the opportunity to work on the intersection of machine learning and causal inference, developing statistical and deep learning techniques that capitalize on the identification of the generative causal structure associated with data.
There is a growing interest in machine learning applications in incorporating causal knowledge to representation learning, which can improve the interpretability and fairness of models, and help to construct robust representations for domain adaptation.
We will investigate the use of machine learning methods to extract latent features from data and their application for causal inference.
The project will then explore the optimisation of neural networks architectures to produce representations that capture the causal structure underlying data and will develop causally-informed models with causal predictive power, which is crucial for many applications, from medical treatments to decision-making for climate or socio-economic policies.
The work of the student will be embedded in a broader collaborative research line investigating the extraction of causal information from hidden features present in high-dimensional data, and their exploitation for modelling and prediction of causal effects.
Furthermore, the student will benefit from computing resources and expertise at the Dept. of Computer Science, in particular at the Artificial Intelligence Research Centre, CitAI, which gathers researchers with wide experience in machine learning, deep learning, artificial intelligence, and cognitive science.
The results of a successful doctoral thesis will lead to an improved design of causally-informed models, which can have a substantial impact on life and medical sciences. Technological applications will also greatly benefit from representation learning methods with augmented generalization properties across domains.
The student will be encouraged to publish the results of their research at leading international conferences and in top-tier machine learning journals.
More broadly, the proposed research will afford the student a chance to go deep in the areas comprising machine learning, statistical learning, causal inference, or deep learning, and the acquisition of managerial skills to develop, manage, and communicate the research findings.
This research line will endow the student with knowledge and skills constituting a robust foundation for an academic career and moreover highly appreciated in industry.
Eligibility and requirements
The candidate should have an upper second class honours BSc/BEng/MEng (or equivalent, or higher) degree in computer science, data science, physics, or mathematics. They should demonstrate aptitude for original research.
The candidate should possess a good understanding in some areas comprising statistical learning, deep learning, causal analysis, or data analysis.
A candidate who demonstrates exceptional aptitude in one or more of these areas (as evidenced, for instance, through strong academic credentials or research papers in reputable, peer-reviewed journals/conferences) may be accorded preference. The successful candidate should be able to code comfortably in Python. Matlab is also desirable.
A doctoral candidate is expected to meet the following pre-requisites for their PhD:
- Demonstrate a sound knowledge of their research area
- Achieve and demonstrate significant depth in at least a few chosen sub-areas relevant to their primary research area
- Demonstrate the ability to conduct independent research, including a critical assessment of their own and others’ research
Previous publications in high-quality papers in reputable peer-reviewed conferences and journals are desirable.
The studentship is for 3 years and will provide full coverage of tuition fees (Home and Overseas) and an annual tax-free stipend of £12,000.
Each student would also have the opportunity to earn around £2.2K pa on an average (max. is around £4.3K pa) through a teaching assistantship. We shall prioritise these scholarship holders while allocating the teaching assistantships.
How to apply
Initial informal enquiries should be addressed to Daniel Chicharro.
Visit our Computer science research degrees web page for further information on making a formal application.
When submitting your application, enter the title “Improving Predictive Models with Causal Structure Learning” and you will automatically be considered for this studentship.
You do not need to submit a proposal as part of your application as the project has already been outlined.
The online application can be found in the ‘How to apply section’ in the web link above and should include the following supporting documents:
- Copies of Degree Certificates and Transcripts in official English translation - original will be requested before an offer is made.
- Official work e-mail addresses (not private ones) for two referees (one of which must be an academic).
- Proof of English Language proficiency (minimum average score of 6.5 IELTS, with a minimum of 6.0 in each of the four components) if English is not your first language.
The outcome of the selection process should be announced by the end of June. The successful candidate will formally start their doctorate either in July or in October 2022.
For queries regarding the application process, please email the School.
Equality, diversity and inclusion
City, University of London is committed to promoting equality, diversity and inclusion in all its activities, processes, and culture, for our whole community, including staff, students and visitors.
We welcome applications regardless of gender, sexual orientation, disability, marital status, race, nationality, ethnic origin, religion or social class. For more information on our approaches to encouraging an inclusive environment, please see our Equality, Diversity and Inclusion pages.