Up to 30% off — ends 2 Aug
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Up to 30% off — ends 2 Aug
ONLY00h00m00s
M Khan
3 months ago
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Quantum Machine Learning for Financial Fraud Detection: Generative Modelling and Adversarial Robustness on NISQ Quantum Processors Aston University in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Full funding availableDeadline
Jun 1, 2026
Country
United Kingdom
University
Aston University

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About this position
This PhD project at Aston University, within the College of Engineering and Physical Sciences, explores the intersection of quantum machine learning and financial fraud detection. Financial crime, including payment fraud, money laundering, identity theft, and insider trading, poses significant risks to global economies. The research aims to develop generative modelling and adversarial robustness techniques on NISQ (Noisy Intermediate-Scale Quantum) quantum processors, leveraging advances in artificial intelligence, cyber security, and quantum computing.
Supervised by Dr M Khan and Prof J Alcaraz Calero, the project will focus on innovative approaches to detect and mitigate financial fraud using quantum-enhanced machine learning algorithms. The research will involve designing and implementing quantum generative models, evaluating their performance against adversarial attacks, and assessing their robustness in real-world financial scenarios. The project is highly interdisciplinary, combining expertise in computer science, mathematics, statistics, information technology, and physics.
Applicants should have a strong academic background in relevant fields, with excellent programming skills and familiarity with machine learning, quantum computing, or cyber security. The project offers a unique opportunity to contribute to cutting-edge research at the interface of quantum technologies and financial security. While funding details are not specified, candidates are encouraged to check for potential scholarships or funding options at Aston University.
The application deadline is 1 June 2026. Interested candidates should apply online via the FindAPhD project link, ensuring they meet the entry requirements and prepare all necessary documents. For further information, prospective applicants may contact the supervisors directly.
Funding details
Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.
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