Job Description

Federated learning (FL) is a privacy-preserving distributed learning paradigm that allows different clients to create a shared AI models without having to share their data. Despite these advantages, conventional FL frameworks are not exempt of security weaknesses. The role involves performing research to create on decentralised federated learning that 1) improved robustness against poisoning and adversaries whilst 2) while maintaining their privacy preserving properties.
Specifically, the role will involve investigating:

• Decentralised FL convergence and vulnerabilities
• Novel decentralised robust aggregation technique considering local consensus by clustering the nodes basedon similar data distribution
• Novel decentralised robust aggregation technique considering local consensus by clustering the nodes basedon local trust and consensus

About the person:
To be considered for interview, applicants must demonstrate that they meet the essential criteria for the post, which includes:

• Have or about to obtain a PhD in computer science, engineering, mathematics or physical sciences area
• Recent high quality research experience in machine learning/AI, or both, as evidenced by a strong track record of publications in leading journals and conferences in relevant areas.
• Software programming skills

To be successful at shortlisting stage, please ensure you clearly evidence in your application how you meet the essential and, where applicable, desirable criteria listed in the Candidate Information.
Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.
What we offer:
Beyond a competitive salary, the University offers an attractive benefits package including a holiday entitlement of up to 8.4 weeks a year, pension schemes and development opportunities. We support staff wellbeing with flexible working options, work-life balance initiatives and support for physical and mental health. You can find more detail on all of this and more at .
Queen's University is committed to promoting equality of opportunity to all. We subscribe to Equality Charter Marks such as the Diversity Charter Mark NI and Athena Swan and have established staff networks such as iRise (Black, Asian, Minority Ethnic and International Staff Network) and PRISM (LGBTQ+) which help us progress equality.
For further information on our commitment to Equality, Diversity and Inclusion, please visit .
If you are an international applicant and don't already hold a visa that permits you to take up the role you are applying for, please use the information provided on our website to self-assess whether the University is likely to be able to support a visa application - .