Research Associate / Senior Research Associate - Machine Learning for Financial Investment Management (Stratlib.AI)

The role

This fixed-term research post is part of the Stratlib.AI project funded by Innovate UK.

Stratlib.AI aims to offer businesses a flexible and trustworthy platform to generate bespoke investment analytics, optimised using cutting edge artificial intelligence (AI) / machine learning (ML) techniques. The Stratlib.AI consortium includes financial industry partners and academic partners at the University of Bristol and the University of Birmingham.

Based at the University of Bristol, this research post will focus on developing a suite of configurable machine learning models suitable for classification and regression tasks in the application areas of asset management and credit management.

Essential skills include expertise in machine learning applications and theory. Candidates with experience working with timeseries data and/or financial datasets will be considered favourably.  

What will you be doing?

The role holder will be responsible for developing a suite of models offering varying levels of complexity and interpretability (e.g., linear regression and Bayesian models, decision trees and random forests, and deep neural network approaches).

Working in collaboration with industry and academic partners, the successful candidate will co-develop interactive tools for non-expert users to select, configure, train, and test these machine learning models while offering guidance and best practice. Outputs will be incorporated into a commercial platform for financial investment management.

You should apply if

You have

• BSc or MSc degree in computer science, or closely related field.
• PhD in Computer Science or related discipline (or near completion), with a focus on applications of machine learning.
• Expertise in AI / machine learning applications in finance.
• Expertise in machine learning for timeseries data.

Please read the attached Job Description for further details.

Additional information

Salar: Grade I £37,099 – £41,732 per annum; Grade J £41,732 - £46,974 per annum

For informal queries please contact Dr John Cartlidge, email: [email protected] .

To find out more about what it's like to work in the Faculty of Engineering, and how the Faculty supports people to achieve their potential, please see our staff blog:

https://engineeringincludesme.blogs.bristol.ac.uk/

Interviews are anticipated to be held on 13 May 2024.

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The University of Bristol aims to be a place where everyone feels able to be themselves and do their best in an inclusive working environment where all colleagues can thrive and reach their full potential. We want to attract, develop, and retain individuals with different experiences, backgrounds and perspectives – particularly people of colour, LGBT+ and disabled people - because diversity of people and ideas remains integral to our excellence as a global civic institution.