PhD Scholarships

The centrepiece of the project will be the investigation of early detection of SARS-Cov-2 viruses in clinical conditions, from chest CT scans and x-rays. This project will develop new solutions for segmenting lesions and predicting COVID-19 and other types of pneumonia from chest CT scans using an advanced deep-learning methodology.

Eligibility and requirements

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

• The candidate should have an upper second-class MSc/MEng (or equivalent, or higher) degree in Computer Science, Mathematics, Statistics, Biomedical Engineering or similar.
• They should demonstrate aptitude for original research and understand image processing, computer vision, numerical algorithms, computational statistics, machine learning, deep learning, object detection and segmentation well.

How to Apply

Applicants are welcome to discuss their application in advance with Prof. Eduardo Alonso, E.Alonso@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 enter the title of the research project: Development and implementation of a unified framework for the lesion detection and classification of SARS-COV-2 types of viruses in chest CT scans and CXRs. The entry point will be in October or later.

AI systems, and in particular Deep Neural Networks have obtained impressive results, from defeating professional players at games to the recognition of protein 3D structures. However, they are restricted to specific tasks and domains. In technical terms, they are good at interpolating data, but not at classifying and predicting data dissimilar to their training datasets. That is, AI does not generalize and transfer knowledge.

Such deficit poses, in turn, the question of explainability. Since AI systems don’t show coherent behaviour across comparable domains and tasks, it is difficult to interpret how they operate and thus account for their results.

The project aims at answering the following questions:

• Can AI systems that use categorical structures improve generalization and facilitate knowledge transfer across heterogeneous datasets?
• Can AI systems with richer representations beyond sets and equivalence relations recognize similarities across different domains and tasks?
• Can such systems be a path towards Artificial General Intelligence?
• Can we build explainable AI systems based on the formal foundations of category theory?
• Can category theory serve as a tool to analyse and interpret the operations of AI systems?
• Can category theory be instrumental in building accountable and responsible AI systems?

Eligibility and requirements

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

• The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in Mathematics/Computer Science.
• They should demonstrate aptitude for original research and possess a good understanding of category theory and machine learning. Ideally, the successful candidate should have proven skills coding in Python.

How to Apply

Applicants are welcome to discuss their application in advance with Prof. Eduardo Alonso, E.Alonso@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 enter the title of the research project: Categorical AI systems. The entry point will be in October or later.

Recent deep learning techniques provide unprecedented accuracy in most medical image analysis tasks, but they typically require large image datasets annotated by medical experts. Studies suggest however that expert radiologists can provide suboptimal annotations in up to 30% of the scans (e.g., labelling a healthy image as pathological or incorrectly localising a lesion).

In the presence of these "noisy labels", state-of-the-art techniques:

1. fail to produce accurate models (due to noisy training samples);
2. cannot be reliably evaluated (due to noisy test samples).

This project will focus on designing and implementing novel noise-robust learning (NRL) techniques for medical image analysis. These techniques will likely build upon recent self-supervision and multi-task learning methods. The candidate will focus both on classification and segmentation tasks, using both public and clinical medical image datasets provided by project partners (e.g., the University of Chicago). The candidate will also explore how NRL techniques can be used in active learning scenarios to identify a selection of wrongly labelled samples to be relabelled by a clinical expert.

The developed approaches will have a sizeable contribution on the deployment of deep learning models in the clinic in human-in-the-loop settings.

Eligibility and requirements

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

• The candidate should have an upper second-class BSc/BEng/MEng (or equivalent, or higher) degree in relevant subjects, including computer science, mathematics, engineering, physics.
• They should demonstrate aptitude for original research. The candidate should possess a good understanding of modern machine learning methods (including self-supervised approaches) and computer vision techniques (from convolutional neural networks to vision transformers). Ideally, the successful candidate should have proven skills in coding in Python, and designing and implementing machine learning pipelines.

How to Apply

Applicants are welcome to discuss their application in advance with Dr. Giacomo Tarroni, giacomo.tarroni@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 enter the title of the research project: Learning from medical images with noisy labels. The entry point will be in October or later.

AI-based computer-aided diagnosis systems for endoscopy video applications are an area of rapid research and development. One application is the real-time computer-aided detection and diagnosis of colorectal polyps during colonoscopy, a crucial examination for the screening and prevention of colorectal cancer (currently the fourth most diagnosed and the third most fatal cancer worldwide).

The School of Science and Technology at City, University of London has partnered with Cosmo IMD (world-leading company in the design of AI tools for endoscopy) to offer a Doctoral Scholarship to investigate novel approaches for endoscopy video analysis. The Scholarship will focus on self-supervised methods and on their power to harness unlabelled data for downstream tasks.

Closing date: 15th of Jun 2023 or until the position has been filled.

The successful candidate will work in an exciting international environment in the heart of the City of London. They will join the CitAI Research Centre (which features academic staff with extensive expertise in machine learning for healthcare) and be able to exploit the power of Hyperion, City’s new High-Performance Computer. They will also have access to part of Cosmo IMD’s proprietary library of annotated endoscopy videos (of unprecedented size, quality, and heterogeneity) and be supported by their expertise in industrial deployment of cutting-edge, FDA-approved AI tools for colonoscopy. The research outputs of this Scholarship will have the chance of impacting the real clinical practice.

Project outline

One of the downsides of deep learning algorithms is that they are significantly data-hungry, requiring vast amounts of manually labeled samples to function correctly and accurate annotations to achieve good performances. In most healthcare applications, including endoscopy video analysis, obtaining such video data is costly (due to the need of medical expertise for accurate annotation) and time-consuming (due to the video length of endoscopy applications often lasting hours). Furthermore, many endoscopy applications focus on the detection and classification of clinical conditions or lesions located in a small fraction of video frames or involve significant class imbalances.

Self-supervised learning refers to methods that use unlabeled data on a pretext task to learn deep models able to capture rich visual data representations without explicit supervision. These pre-trained models are then finetuned with limited labeled data on a downstream task of interest. Self-supervised learning approaches proved to be capable of achieving remarkable results in many computer vision applications, matching or even surpassing the performance of fully supervised models, showing promise to become the new gold-standard pre-training approach for deep learning models. In the same fashion, we envision that successful self-supervised learning on unlabeled endoscopy videos will constitute in the future the building block for the development of high-capacity deep learning models that are robust and accurate on different downstream endoscopy tasks.

What is offered

The doctoral studentship will include:

• An exceptionally competitive annual bursary for 3 years (£24,000/year)
• Full tuition fees for UK Students (and partial coverage for EU and International ones).
• The opportunity to earn up to around £4,300/year through a non-compulsory teaching assistantship.
• Over £7000 to participate to conferences and training.

Eligibility and requirements

The studentships will be awarded based on 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 (including computer science, mathematics, engineering, physics). 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.
• 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

To apply online, click on this link.

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

The successful candidate will formally start their doctorate either in July or in October 2023.

The PhD application form requires candidates to attach a research proposal. Applicants to this studentship do not need to do so. Instead, they should enter the title of the research project: AI for Endoscopy Video Analysis and supervisor Dr. Giacomo Tarroni. The entry point will be in October or later.

The School of Science and Technology at City, University of London, in collaboration with its industrial partner 4BetterDevices GmbH, are offering a doctoral studentship for a project aiming at the development and application of machine learning methods for feature selection and sensitivity analysis in biomedical data. 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: 30 Jun 2023 or until the place has been filled.

The successful candidate will work on the intersection of machine learning and data analysis, developing statistical and machine learning techniques to analyze biomedical data. High-dimensional data sets are increasingly collected for medical diagnosis and in biological research and an important question regards characterising how information relevant for a certain medical purpose is distributed among the factors composing the data, and how to extract and use relevant information considering privacy and ethical constraints. The student will contribute developing tools to improve feature selection methods for medical diagnosis, and to improve the identification and separation between medically relevant sources of information in data and nuisance features that may compromise privacy or introduce unwanted discriminatory biases in the models or medical procedures. The student will also have the opportunity to investigate the application of these methods to better understand the distribution of information and representations formed in neural networks, advancing towards more transparent deep learning models for medical decision-making.

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 data analysis.

What is offered

The doctoral studentship will provide:

• An annual tax-free stipend for three years of £19,000.
• Full coverage of tuition fees (6,240 per year) for three years.
• The opportunity to additionally earn through teaching assistantship around £2,200 per year on average (max. is around £4,300 per year). We shall prioritise scholarship holders while allocating teaching assistantships.

Eligibility and requirements

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

• The candidate should have a UK home student status.
• The candidate should have an upper second-class BSc/BEng/MSc/MEng (or equivalent, or higher) degree in Mathematics, Computer Science, Statistics, Engineering, Physics, or related STEM areas.
• The successful candidate should preferably have some previous knowledge of machine learning methods and skills coding in Python and/or Matlab.

How to Apply

All applications submitted by June 30 will be considered. Applications will still be accepted after, until the position is filled.

Applicants are welcome to discuss their application in advance with Dr. Daniel Chicharro, Daniel.Chicharro@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 enter the title of the research project: Identifying and processing information sources for medical applications under privacy and fairness constraints and supervisors Prof. Eduardo Alonso and Dr. Daniel Chicharro. The entry point will be in October or later.

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

Eligibility

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

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:

Eligibility

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

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:

Eligibility

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

→ 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:

Eligibility

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

→ 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