Research Associate

Grade UE07: £39,347 - £46,974 per annum

School of Informatics / Science and Engineering

Full time: 35 hours per week

Fixed Term: For 24 months

The School of Informatics, University of Edinburgh invites applications for Research Fellow in NLP and large language model-based financial asset management and decision support systems.

The Opportunity:

A post-doctoral fellow on AI, NLP and financial computing will be recruited for 24 months for assisting with the execution of the project. The candidate will undertake original research in AI-driven Financial Computing, within the School of Informatics in collaboration with School of Mathematics and the Centre for Investment Innovation.

The project RA improves a deeper understanding of potential risks/uncertainties to which firm-based - risk factors are measured, hence unlocking the potential for significant improvements in the fields of finance investment management, and decision-making. The project will also aim to provide suitable benchmarking methods to evaluate our proposed AI/NLP/LLM models against commonly used and state-of-the-art AI models as well as evaluation metrics.

Your skills and attributes for success: 

Essential:

PhD and experience in Finance. AI/NLP Large Language Models (GPT) or Quantitative Finance/Financial Computing background(e.g. ChatGPT for asset risk management). Candidates who are close to completing their PhD, and who otherwise meet the selection criteria, will be considered. Proven experience of undertaking original research with a strong focus on financial asset management, portfolios or price forecasting. Proven experience of work with industry collaborators, particularly related to finance sectors. A track record of first author and/or collaborative publications in high quality journals and international conferences (Finance, AI, NLP related publications). Strong relevant research skills in running large scale NLP models (e.g. BERT, GPT) for financial text analysis. The candidate shall have strong research skills of using LLM-based models for firms financial Information summary. Strong software (e.g. Python, Pytorch and relevant GitHub packages) and experiment testbed development experience (e.g. benchmarking NLP/ML models, using ChatGPT for document analysis, financial asset price forecasting). The candidate shall have experience in benchmarking and software implementation prototype. Effective written and oral communication skills in a range of contexts (e.g. research presentations and publications). Experience of contributing to co-supervise UG/MSc students as well as in more specialist areas of AI/NLP and Financial Computing. Experience of engaging with a range of wider communities such as financial industry sectors, asset management firms and wider research/industry communities.

Desirable:

Proficiency in Linux, physics simulators (Gazebo), ROS1/ROS2. Experience in application of foundation models in computer vision. Background in Soft/Flexible Robotics