PhD Scholarships

We are seeking to appoint a PhD student to be affiliated to the Artificial Intelligence Research Centre, working with the sponsor, Bosch ASS Ltd. We are offering a four-year doctoral studentship commencing on the 1st October, 2022.

Closing date: June 30th 2022

The successful candidate will participate in a project titled “Advanced Diagnostic Technician Support PART Forecasting”. The project is focused on developing a self-learning platform based on vehicle data, to recognize patterns of failure and make suggestions to a technician workshop.

What is offered

Full coverage of tuition fees for 4 years for both UK and Overseas students.

A stipend of £18,000 per year, for 4 years.

Eligibility

The studentship will be awarded based on outstanding academic achievement and the potential to produce cutting edge research. Prospective applicants must:

Hold a good Master’s degree (no less than a second class honours degree or an equivalent qualification) in Computer Science with a specialization in Artificial Intelligence. We will also consider applications from those with a good honours degree in Computer Science or extensive professional experience in the area;

Proficiency in implementing and evaluating deep learning architectures and algorithms;

Proficiency in programming in Python;

Experience working with cloud platforms such as AWS for model deployment;

Be able to demonstrate proficiency in the use of oral and written English.

How to Apply

Applicants are welcome to discuss their application in advance with Dr. Esther Mondragón, E.Mondragon@city.ac.uk.

If you wish to apply, you can do it here

The PhD application form requires candidates to attach a research proposal. Applicants to this studentship do not need to do so. Instead, they should attach a document stating that they are applying to the Bosch Advanced Diagnostic Support Industrial PhD scholarship.

The School of Mathematics, Computer Science, and Engineering at City, University of London is offering a doctoral studentship for a project aiming at the application of causal structure learning to improve predictive modelling and representation learning techniques. The successful candidate will be affiliated to the Artificial Intelligence Research Centre and benefit from cutting-edge expertise in research areas comprising machine learning, deep learning, artificial intelligence, and cognitive science.

Closing date: 15th of May 2022

Project outline

The successful candidate will 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. This work responds to growing demand in the market of machine learning applications to incorporate causal knowledge for representation learning, in order to improve the interpretability and fairness of models and to construct robust representations for domain adaptation.
The student will investigate the use of machine learning methods to extract and exploit causally-informative latent features from data and explore the optimisation of neural networks architectures to produce representations that capture the causal structure underlying data. Ultimately, the student will contribute to developing more powerful models enriched with causal predictive power, which is crucial for applications ranging from the design of medical treatments to decision-making for the climate or socio-economic policies. Technological applications will also greatly benefit from advances in causal representation learning that offer augmented generalization properties across domains. This research line will endow the student with knowledge constituting a robust foundation for an academic career and with a set of skills highly appreciated in industry, acquiring cutting-edge expertise in the intersection of machine learning, statistical learning, deep learning, and causal inference.

What is offered

A doctoral studentship will provide:

Full coverage of tuition fees for 3 years for both UK and Overseas students.

An annual tax-free stipend for 3 years of £12,000.

The opportunity to additionally earn through teaching assistantship around £2.2K per year on average (max. is around £4.3K per year). We shall prioritise these scholarship holders while allocating the teaching assistantships.

Eligibility

The studentships will be awarded based on outstanding academic achievement and the potential to produce cutting-edge research. Prospective applicants should:

Hold a second- or higher-class honours BSc/BEng/MEng (or equivalent, or higher) degree in computer science, data science, physics, or mathematics.

Possess a good understanding in some areas comprising statistical learning, deep learning, causal analysis, or data analysis.

Be able to code comfortably in Python. Matlab is also desirable.

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.

How to Apply

Applicants are welcome to discuss their application in advance with Dr Daniel Chicharro, Daniel.Chicharro@city.ac.uk
Further details on the project, eligibility, and how to apply can be found at the webpage of School of Mathematics, Computer Science, and Engineering Doctoral Studentships here.

If you wish to apply, you can do it here.

When submitting your proposal, enter the title “Improving Predictive Models with Causal Structure Learning” and you will automatically be considered for this studentship.

The School of Mathematics, Computer Science and Engineering at City, University of London is offering a three-year doctoral studentship for 2021/22 entry. The scholarship is one of two co-funded by Equideum Health, a company working at the interface between blockchain, machine learning and privacy enhancing technologies to develop new healthcare solutions. The successful candidate will be supported by the Department of Computer Science and its academic staff (with extensive expertise in machine learning in healthcare) as well as deal with real-world challenges relative to industrial deployment.
Applications are invited from exceptional UK, European and International graduates wishing to pursue cutting-edge research in AI and healthcare, one of the School's key research areas. The School is investing in academic excellence following its success in the last REF, which highlighted the world class quality of its research.

Closing date: March 21st 2022

Project outline

When querying an API or delegating the execution of an application to a third-party server, we are trusting the remote machine to execute the application or request correctly, and not return a "cheaper" result obtained by running a degraded version of the application that would consume less resources.
While this is generally not an issue when interacting with widespread public cloud operators who have their reputation at stake, the emergence of decentralised cloud platforms (iExec, Golem, ...) changes this assumption. In a decentralised cloud, the cloud operator does not run the infrastructure but instead aggregates compute resources from a large number of more or less reliable sources. This suggests a need to verify the correct execution of applications delegated to decentralised environments.
Zero-Knowledge proofs are becoming increasingly popular for their ability to help blockchains scale up and bring privacy to their transactions (Z-Cash, AZTEC, ...). Any kind of computation can be verified using ZKPs as long as one can express them in a suitable form. They remain however computationally demanding.
The goal of his PhD project is to develop various approaches including ZKPs to verify the computation of modern machine learning algorithms. Our primary application use-case will target models used for predictions in healthcare, including signal and image analyses tasks for classification and pattern recognition. The project will focus initially on verifying integrity for inference-time computations but potentially also develop methods for training algorithms. Execution integrity can open the doors to multiple improvements to the provider-payers interactions in the health insurance domain for instance.

What is offered

A doctoral studentship will provide:

An annual bursary (£16,000) with a substantial top-up salary offered by the company to outstanding candidates, for a total amount aligned to typical RA salaries in the UK.

Full tuition fee for Home students. Applications from international applicants are welcome but the applicant must make appropriate arrangements to cover the difference between the international and home tuition fee.

A budget for travel expenses and consumables (£4,000 in total).

Eligibility

The studentships will be awarded on the basis of outstanding academic achievement and the potential to produce cutting edge-research. Prospective applicants must:

Hold a good honours degree (normally no less than a second class honours degree or an equivalent qualification) in an appropriate subject. Exceptionally, if the first degree is in a different subject area, we can consider applications from those with a good Master’s Degree in a relevant subject or extensive professional experience in the area of their proposed research;

Be able to demonstrate proficiency in the use of oral and written English;

Applicants whose mother tongue is not English must meet any one or a combination of the following:
- A minimum IELTS average score of 6.5; with a minimum of 6.0 in each of the four components
- The award of a Masters’ degree, the teaching of which was in English from an English Speaking country.

How to Apply

Applicants are welcome to discuss their application in advance with the academic supervisor in the School Dr Giacomo Tarroni, Giacomo.Tarroni@city.ac.uk.

If you wish to apply, you can do it here

The School of Mathematics, Computer Science and Engineering at City, University of London is offering a three-year doctoral studentship for 2021/22 entry. The scholarship is one of two co-funded by Equideum Health, a company working at the interface between blockchain, machine learning and privacy enhancing technologies to develop new healthcare solutions. The successful candidate will be supported by the Department of Computer Science and its academic staff (with extensive expertise in machine learning in healthcare) as well as deal with real-world challenges relative to industrial deployment.
Applications are invited from exceptional UK, European and International graduates wishing to pursue cutting-edge research in AI and healthcare, one of the School's key research areas. The School is investing in academic excellence following its success in the last REF, which highlighted the world class quality of its research.

Closing date: March 11th 2022

Project outline

Federated machine learning is a technique to incrementally train a model using different datasets, owned by different entities. This is the technique used by Apple/Google on our smartphones to improve their models' accuracy and "improve your user experience".
We envision Federated Learning will dramatically change the way we collaborate in healthcare settings. In particular, the ability for multiple entities to share their knowledge within a federated learning network could open collaboration opportunities between more and more institutions across the world.
The challenge lies in the discrepancy between datasets at each node in the network (each entity). If different nodes have different data distributions, the learned global model will provide sub-optimal local accuracy. The goal of his PhD project is to develop a way to best provide personalised models in the common setting where nodes aim to jointly solve a common problem. In another form of collaborative learning, each entity within the network might want to benefit from the backbone features learnt by the network but apply this knowledge to its custom task, in a Federated Multi-Task Learning fashion.
The project will involve publicly available medical datasets (e.g., UK Biobank), mostly consisting (but not limited to) medical scans acquired at different institutions. Common tasks such as classification, segmentation and object detection will be used as test scenario for the solutions developed.

What is offered

A doctoral studentship will provide:

An annual bursary (£16,000) with a substantial top-up salary offered by the company to outstanding candidates, for a total amount aligned to typical RA salaries in the UK.

Full tuition fee for Home students. Applications from international applicants are welcome but the applicant must make appropriate arrangements to cover the difference between the international and home tuition fee.

A budget for travel expenses and consumables (£4,000 in total).

Eligibility

The studentships will be awarded on the basis of outstanding academic achievement and the potential to produce cutting edge-research. Prospective applicants must:

Hold a good honours degree (normally no less than a second class honours degree or an equivalent qualification) in an appropriate subject. Exceptionally, if the first degree is in a different subject area, we can consider applications from those with a good Master’s Degree in a relevant subject or extensive professional experience in the area of their proposed research;

Be able to demonstrate proficiency in the use of oral and written English;

Applicants whose mother tongue is not English must meet any one or a combination of the following:
- A minimum IELTS average score of 6.5; with a minimum of 6.0 in each of the four components
- The award of a Masters’ degree, the teaching of which was in English from an English Speaking country.

How to Apply

Applicants are welcome to discuss their application in advance with the academic supervisor in the School Dr Giacomo Tarroni, Giacomo.Tarroni@city.ac.uk.

If you wish to apply, you can do it here