12945 - Research Assistant in Computational Finance
| Dyddiad hysbysebu: | 22 Awst 2025 |
|---|---|
| Cyflog: | £34,610 i £39,906 bob blwyddyn |
| Oriau: | Llawn Amser |
| Dyddiad cau: | 03 Medi 2025 |
| Lleoliad: | Edinburgh, Scotland |
| Gweithio o bell: | Ar y safle yn unig |
| Cwmni: | University of Edinburgh |
| Math o swydd: | Cytundeb |
| Cyfeirnod swydd: | 12945 |
Crynodeb
Grade UE06: £34,610 - £39,906 per annum
CSE / School of Mathematics
Full-time: 35 hours per week
Fixed-term: for 1 year
Applications are invited for the post of Research Associate (RA) in Computational Finance in the School of Mathematics, University of Edinburgh. The post is available from 1st November 2024 for a fixed term of 1 year, full-time contract.
The position is funded by the IORH grant: “DeFi risk metrics via multi-agent simulations”. The RA is to work in interdisciplinary fashion with researchers in both Informatics and Mathematics. This project will develop principled risk management systems to improve economic security in decentralised finance using established tools from financial mathematics / quantitative finance / stochastic control / game theory augmented by agent-based and reinforcement learning based simulations.
To maximise innovation and creativity in informatics / mathematics, applications from candidates with diverse career paths, including those returning from a career break or following time in other roles, are encouraged.
The Opportunity:
The Schools of Informatics and Mathematics at the University of Edinburgh are at the forefront of research in computational finance / financial mathematics / quantitative finance / reinforcement learning. The successful candidate will work with the IORH grant holders: Dr Aris Filos-Rastikas (Informatics), Dr David Siska (Mathematics), Prof Lukasz Szpruch (Mathematics) on developing state-of-the-art theory and simulation techniques for automated risk-management in decentralised finance. The PDRA will be encouraged to publish and present their results as well as to develop open-source software tools.
Applicants should have a working knowledge in at least one of the following areas: computational finance, mathematical finance, reinforcement learning, stochastic control, game theory or related areas of mathematics / informatics / computer science. They should have a track record of or potential for research excellence. They should be enthusiastic about software development and have working knowledge of python and relevant deep-learning libraries (e.g. torch).
Please upload a CV and a research statement outlining how your current research connects with the position (no more than 2 pages) when applying for the post via the online recruitment system. In addition please arrange for at least 2 references to be sent direct to references@maths.ed.ac.uk quoting the reference number 12945.
Your skills and attributes for success:
An MSc or PhD in agent-based modelling, game theory, mathematical finance, reinforcement learning, stochastic control or related areas of mathematics / informatics / computer science.
Track record of or potential for research excellence, evidenced by e.g. preprints / publication record.
Enthusiasm about software development, evidenced by e.g. projects on Github.
Interest in decentralised finance and strong motivation to develop tools to measure, model and understand risk in decentrilsed finance protocols.
CSE / School of Mathematics
Full-time: 35 hours per week
Fixed-term: for 1 year
Applications are invited for the post of Research Associate (RA) in Computational Finance in the School of Mathematics, University of Edinburgh. The post is available from 1st November 2024 for a fixed term of 1 year, full-time contract.
The position is funded by the IORH grant: “DeFi risk metrics via multi-agent simulations”. The RA is to work in interdisciplinary fashion with researchers in both Informatics and Mathematics. This project will develop principled risk management systems to improve economic security in decentralised finance using established tools from financial mathematics / quantitative finance / stochastic control / game theory augmented by agent-based and reinforcement learning based simulations.
To maximise innovation and creativity in informatics / mathematics, applications from candidates with diverse career paths, including those returning from a career break or following time in other roles, are encouraged.
The Opportunity:
The Schools of Informatics and Mathematics at the University of Edinburgh are at the forefront of research in computational finance / financial mathematics / quantitative finance / reinforcement learning. The successful candidate will work with the IORH grant holders: Dr Aris Filos-Rastikas (Informatics), Dr David Siska (Mathematics), Prof Lukasz Szpruch (Mathematics) on developing state-of-the-art theory and simulation techniques for automated risk-management in decentralised finance. The PDRA will be encouraged to publish and present their results as well as to develop open-source software tools.
Applicants should have a working knowledge in at least one of the following areas: computational finance, mathematical finance, reinforcement learning, stochastic control, game theory or related areas of mathematics / informatics / computer science. They should have a track record of or potential for research excellence. They should be enthusiastic about software development and have working knowledge of python and relevant deep-learning libraries (e.g. torch).
Please upload a CV and a research statement outlining how your current research connects with the position (no more than 2 pages) when applying for the post via the online recruitment system. In addition please arrange for at least 2 references to be sent direct to references@maths.ed.ac.uk quoting the reference number 12945.
Your skills and attributes for success:
An MSc or PhD in agent-based modelling, game theory, mathematical finance, reinforcement learning, stochastic control or related areas of mathematics / informatics / computer science.
Track record of or potential for research excellence, evidenced by e.g. preprints / publication record.
Enthusiasm about software development, evidenced by e.g. projects on Github.
Interest in decentralised finance and strong motivation to develop tools to measure, model and understand risk in decentrilsed finance protocols.