Job Description

UE07: £40,247.00 - £47,874.00 Per Annum.

CAHSS / Business School.

Fixed Term Contract - 19 Months.

Full Time - 35 Hours Per Week.

The Opportunity

This post is full-time (35 hours per week); however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working. We are seeking to recruit a Postdoctoral Research Fellow for the project titled “Early Warning Signal Detection for Corporate Risk Using Machine Learning within a Merton Framework,” sponsored by Abrdn. The selected candidate will be stationed at the University of Edinburgh Business School and collaborate closely with the project leaders. The candidate will develop a new model that merges the Merton approach to measure corporate risk with machine learning techniques, such as complex neural network infrastructure. Responsibilities include data analysis, developing models, and contributing to the dissemination and publication of research findings. The role also involves working in tandem with Nick Kordowski’s team in the Fixed Income division at Abrdn. This position presents an excellent opportunity for the candidate to contribute to high-impact research and publish in top ranking academic journals.

Your Skills And Attributes For Success

A PhD (or final submission) in Statistics, Operations Research, Computational Sciences or Economics, Management Science or closely related field.
Experience of designing and performing data analyses.
Excellent programming skills in Python.
Excellent written and verbal communication skills.
Demonstrated ability to publish in high quality peer-reviewed, academic journals.

Click here to view a copy of the full Job Description.

Application Information

Please ensure you include the following documents in your application:

CV
Cover letter
Proof of Academic Qualification

As a valued member of our team, you can expect:

A competitive salary of £40,497-£48,149
An exciting, positive, creative, challenging and rewarding place to work.
To be part of a diverse and vibrant international community
Comprehensive Staff Benefits, such as a generous holiday entitlement, competitive pension schemes, staff discounts, and family-friendly initiatives. Check out the full list on our staff benefits page (opens in a new tab) and use our reward calculator to discover the total value of your pay and benefits

Championing equality, diversity and inclusion

The University of Edinburgh holds a Silver Athena SWAN award in recognition of our commitment to advance gender equality in higher education. We are members of the Race Equality Charter and we are also Stonewall Scotland Diversity Champions, actively promoting LGBT equality.

Prior to any employment commencing with the University you will be required to evidence your right to work in the UK. Further information is available on our right to work webpages (opens new browser tab)

Key dates to note

The closing date for applications is 17 February 2025.

Unless stated otherwise the closing time for applications is 11:59pm GMT. If you are applying outside the UK the closing time on our adverts automatically adjusts to your browsers local time zone.

Interviews will be held 10March 2025.

The University may be able to sponsor the employment of international workers in this role.  This will depend on a number of factors specific to the successful applicant.

About Us

As a world-leading research-intensive University, we are here to address tomorrow’s greatest challenges. Between now and 2030 we will do that with a values-led approach to teaching, research and innovation, and through the strength of our relationships, both locally and globally.

About The Team

The aim of University of Edinburgh Business School is to be a world-class Business School within a world-class University. We are distinctive, fostering a unique coaching culture, where all students have access to coaching and much of this is facilitated by peer support leaders from the student cohorts. The Business School is focused on sustainability, aiming to anchor the UN Sustainable Development goals into our strategic planning and developing our activities in line with the University’s target to be carbon-neutral by 2040. Furthermore we are actively participating in data-driven innovation which envisages Edinburgh becoming the Data Capital of Europe.

Based in the heart of Edinburgh, staff at the Business School are immersed in the vibrant life of the city. The Business School has excellent transport links including buses and a location that is only a 20 minute walk from Edinburgh Waverley station. In the summer we find ourselves with easy access to the Edinburgh International Festival and the Edinburgh Festival Fringe. For a breath of fresh air all year round, green spaces such as George Square and the Meadows are right outside our front door.

The culture of the Business School is people orientated and diverse; we have a strong Equality and Diversity committee made up of both academic and professional services staff and we are committed to the principles of Athena SWAN https://www.advance-he.ac.uk/equality-charters/athena-swan-charter . We also support staff in a hybrid working environment and offer the possibility for most roles to work in a combination of on-campus and at-home working where suits both them and the organisation.

Academically, we are an interdisciplinary School with particular strengths in management science, leadership organisations and society, interdisciplinary accounting, corporate finance, climate change and sustainability, and entrepreneurship. The School is accredited by the Association to Advance Collegiate Schools of Business (AACSB), the EFMD Quality Improvement System (EQUIS) and the Association of MBAs (AMBA). This triple accreditation highlights the quality of teaching and excellence of research at the School.

Detailed information can be found at the School website at www.business-school.ed.ac.uk

The University of Edinburgh

New Jobs

Research Fellow in Corporate Risk & Machine Learning