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Northeastern University London Fully Funded PhD Scholarship in Computer Science: Reliable What-If Analysis of Intelligent Networks

Northeastern University London Faculty of Computing, Mathematics, Engineering & Natural Sciences
✓ Fully Funded ⏰ Closing Soon 🎓 Artificial Intelligence 🎓 Computer Science 🎓 Control Systems 🎓 Networks 🎓 Statistics machine learning autonomous systems agentic AI causal inference counterfactual analysis decision support intelligent networks

This fully-funded PhD at NU London develops statistically reliable frameworks for what-if analysis in intelligent networks. The research combines causal inference, conformal prediction, and agent-environment modeling to enable trustworthy decision support and debugging of autonomous systems.

Project Description

The project focuses on designing a unified framework for reliable counterfactual analysis in intelligent, agentic networks. Key aspects include: Developing statistical and AI-driven methods for what-if analysis in autonomous and agentic systems. Combining tools from causal inference, agent-based modeling, and statistical learning. Providing robust decision support and debugging tools for intelligent networked systems. Students will benefit from a modern interdisciplinary research environment in London with opportunities to collaborate internationally across the Northeastern University network and the University of Kent.

Entry Requirements

Bachelor’s degree in a relevant subject (2:1 or 1st)
Master’s degree optional
English proficiency: IELTS 6.5 overall (min 6.5 in all components) or equivalent
Open to UK and international students (visa costs not covered)

How to Apply

Submit your application via the NU London application portal
by 01 April 2026, referencing project “R138405”. Include a CV and covering letter outlining your suitability and interest in the project. Shortlisted candidates will be interviewed in May 2026.

Contact for enquiries: o.simeone@nulondon.ac.uk

Eligibility

UK/Home
EU
International

Supervisor Profile

PO
Prof Osvaldo Simeone, Dr Huiling Zhu
Northeastern University London, Faculty of Computing, Mathematics, Engineering & Natural Sciences

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