In the middle of applying to universities? | SHARE YOUR EXPERIENCE In the middle of applying to universities? | SHARE YOUR EXPERIENCE

We have 22 King’s College London, UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence PhD Projects, Programmes & Scholarships for Self-funded Students

Discipline

Discipline

All disciplines

Location

Location

All locations

Institution

Institution

UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence  King’s College London

PhD Type

PhD Type

All PhD Types

Funding

Funding

I am a self funded student


King’s College London, UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence PhD Projects, Programmes & Scholarships for Self-funded Students

We have 22 King’s College London, UKRI Centre for Doctoral Training in Safe and Trusted Artificial Intelligence PhD Projects, Programmes & Scholarships for Self-funded Students

PhD saved successfully

Verification of Matching Algorithms for Social Welfare

Matching is a fundamental problem in combinatorial optimisation with multiple applications in AI, like in belief propagation [10], multi-agent resource allocation algorithms [6], and constraint solving [16], and in economics, like the stable marriage problem [17] and the Adwords market [15], among many other applications. Read more

Integrating Sub-symbolic and Symbolic Reasoning for Value Alignment

An important long-term concern regarding the ethical impact of AI is the so called ‘value alignment problem’; that is, how to ensure that the decisions of autonomous AIs are aligned with human values. Read more

Learning and deploying safe and trustworthy models of data provenance

Our modern lives are increasingly governed by ubiquitous AI systems and an abundance of digital data. More and more products and services are providing us with better tools and recommendations for our professional, personal, and entertainment activities. Read more

Generative modelling with neural probabilistic circuits

The current state of the art in generative modelling is dominated by neural networks. Despite their impressive performance on many benchmark tasks, these algorithms do not provide tractable inference for common and important queries, e.g. Read more

Filtering Results