Re-advertisement: Geometric Deep Learning for Financial Investor Network Analysis
1 month ago
Title
Re-advertisement: Geometric Deep Learning for Financial Investor Network Analysis
Research area and project description
The project is supported by a grant by the Independent Research Fund of Denmark for conducting basic research in Deep Learning-based financial investor network analysis. Work will be focused on proposing new techniques and methodologies, including graph neural networks, convolutional neural networks, and spatio-temporal deep learning models, for financial graphs coming from stock exchanges to detect investment activities, analysis of changes in investor activities over time, and investor community detection. The popularized description of the project is as follows:
The inability of conventional financial models to meet empirical observations from real-world markets has led to a growing level of dissatisfaction. Existing models set unrealistic assumptions for investors' behavioural characteristics and the information flow inside and outside the market. The few recent interdisciplinary studies following data-driven analyses are based on crude behavioural investor categorizations, generic attributes for expressing properties of these investor categories (like age and gender), and simple (linear) mathematical models.
The project will study financial activities at the individual investor level and its evolution over time, based on data-driven methodologies. To achieve this, machine learning approaches traditionally used for graph-based learning and knowledge extraction from graphs will be developed, adapted to the characteristics of financial networks and further be improved for modelling the interaction between individual investors and the market.
Project description (1⁄2-4 pages). This document should describe your ideas and research plans for this specific project. If you wish to, you can indicate an URL where further information can be found.
Qualifications and specific competencesMaster's degree in Computer Engineering, Computer Science, Mathematics, Econometrics, or within a relevant area.Background on machine learning is required.Strong programming skills, e.g., python programming, is desired.Excellent English verbal and written skills.Be able to work well and communicate expert knowledge in an interdisciplinary team and international collaborators.
Place of employment and place of work
The place of employment is Aarhus University, and the place of work is Finlandsgade 22, DK-8200, Aarhus N., Denmark
Contacts
Applicants seeking further information are invited to contact:
Professor Alexandros Iosifidis, How to apply
Please follow this link to submit your application.
Application deadline is 15 May 2024 at 23:59 CEST
Preferred starting date is 1 September 2024
For information about application requirements and mandatory attachments, please see our application guide.
Please note:Only documents received prior to the application deadline will be evaluated. Thus, documents sent after deadline will not be taken into account.The programme committee may request further information or invite the applicant to attend an interview.Shortlisting will be used, which means that the evaluation committee only will evaluate the most relevant applications.
Aarhus University's ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants. All interested candidates are encouraged to apply, regardless of their personal background. Salary and terms of employment are in accordance with applicable collective agreement.
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