A fully funded PhD studentship covering UK home fees and a London stipend of £20,662 per annum for 3 years is available for a starting date in September 2024. The student will be supervised by Dr. Yongxin Yang in the school of Electronic Engineering and Computer Science of the Queen Mary University of London.
This project sits at the intersection of machine learning and quantitative finance, with a focus on advancing areas such as derivatives pricing and portfolio optimization. The research may extend established studies like option pricing [1, 2] and index tracking [3,4] or explore some new topics (e.g., agent-based market simulation).
The successful candidate will harness advanced techniques including Large Language Models (LLMs), AI Agents, Neural Differential Equations, Self-Supervised Learning (SSL), Neural Implicit Representations (NIR), and Meta Learning, with an emphasis on creating accurate, robust, and trustworthy models. The goal is to develop accountable machine learning tools that can be adopted by the finance industry, and promoting open-source research in quantitative finance.
The deadline for applications is the 31st January 2024. To qualify as a home student for the purpose of the scholarships, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship.
Applicants are encouraged to contact Dr. Yongxin Yang at [email protected] with any questions before applying online. More information can be found at http://eecs.qmul.ac.uk/phd/phd-studentships/qm-principal-epsrc-dtp-phd-studentships/principal-and-epsrc-dtp-phd-studentships.
References
[1] Y. Yang and T. Hospedales, "Mixture of Normalizing Flows for European Option Pricing", UAI 2023
[2] Y. Yang and T. Hospedales, "On Calibration of Mathematical Finance Models by Hypernetworks", ECML/PKDD 2023
[3] Y. Yang and T. Hospedales, "Partial Index Tracking: A Meta-Learning Approach", CoLLAs 2023
[4] Y. Yang and T. Hospedales, "An Evaluation of Self-Supervised Learning for Portfolio Diversification", ICANN 2023
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