PhD Studentship: Robustness Analysis and Safety Verification of AI-Based Multiagent Systems in Healthcare Applications

Job Description

Job title: PhD Studentship: Robustness Analysis and Safety Verification of AI-Based Multiagent Systems in Healthcare Applications

Company: City, University of London

Job description: Funding amount: Tuition fees and an annual tax-free stipend of £20,622/year

Hours: Full Time

Applications are invited for a PhD studentship in the Department of Engineering. The successful candidate will have the opportunity to work on analysis of the performance/robustness s for AI-Based Multiagent Systems in Healthcare Applications.

This PhD project aims to enhance the robustness and safety of AI-driven multiagent systems in healthcare. The primary goal is to create methods and tools that guarantee the reliable and safe functioning of these systems in healthcare settings. Key tasks include analysing interactions among AI agents and their surroundings, and ensuring their collective behaviour adheres to strict safety norms. Challenges addressed include maintaining performance consistency under diverse conditions, managing unforeseen agent interactions, and preventing system failures. The project focuses on two key applications: firstly, using multi-robot systems for assisting elderly and disabled individuals in eldercare facilities and homes with tasks like medication reminders and emergency alerting. Secondly, deploying these systems during pandemics for temperature screening, patient triage, and delivering essentials to isolated patients, thereby minimizing human contact and reducing virus spread.

Eligibility and requirements

The candidate should possess a first-class or upper second-class BEng/MEng degree (or equivalent or higher) in Automatic Control or a related field. They should also demonstrate an aptitude for original research.

Scholarship: e.g. The studentship is for 3 years and will provide an annual tax-free stipend that tracks the UKRI rate (actually £20,622 p/a) and tuition fees.

Optional: Additional income: Each student may also have the opportunity to earn around £2,200/year on an average (max. is around £4,300/year) through a teaching assistantship.

In addition, project/consumable costs of £1500 are provided.

The candidate should possess a good understanding of Control Theory and AI algorithms including Deep Reinforcement Learning. 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. Ideally, the successful candidate should have proven skills in Robustness Analysis of Multiagent Systems.

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;

If you are interested in applying, please address any initial informal enquiries by email to Dr. Abdelhafid Zenati

How to apply

Online applications should be submitted by clicking the ‘Apply’ button, above.

For queries regarding the application process, please contact

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 age, caring responsibilities, disability, gender identity, gender reassignment, marital status, nationality, pregnancy, race and ethnic origin, religion and belief, sex, sexual orientation and socio-economic background. City operates a guaranteed interview scheme for disabled applicants.

Tuition fees and an annual tax-free stipend of £20,622/year

Expected salary: £20622 per year

Location: London

Job date: Wed, 10 Jan 2024 02:06:56 GMT

Apply for the job now!