Research Associate: Trustworthy AI for Peer Review Indicators

Heriot-Watt University; Easter Howgate; On-site; GBP 38,000 - 47,000; Part time

Benefits offered by this job

33 days annual leave

9 buildings closed days

Job summary

Heriot-Watt University invites a part-time Research Associate to contribute to a UKRI-funded project on Trustworthy AI for Peer Review. The role focuses on data processing, analysis of REF and publisher data, and testing the Trust Card framework to reveal uncertainty, disagreement and bias in scientometric indicators.

You will work with two other part-time colleagues to develop and validate the Trust Card and to assess novel indicators within an interdisciplinary environment.

Qualifications

• PhD in Computer Science or a closely related discipline.
• Experience of data processing and analytics.
• Interest or experience working with interdisciplinary teams.
• Evidence of self-motivation and ability to work independently.
• Excellent oral and written communication skills.
• Strong time management and ability to meet deadlines.

Responsibilities

• Conduct and disseminate original research aligned with the project and in collaboration with the team.
• Responsible for data processing and analysis of REF data and publisher data, with systematic testing of the Trust Card framework.
• Contribute to evaluation of novel scientometric indicators.
• Support events and team activities as needed.
• Contribute to outreach and impact activities of the project.
• Identify funding sources and help prepare research proposals.
• Demonstrate initiative to manage and achieve project deliverables and deadlines.
• Develop networks and collaborations internally and externally.

Skills

Data processing

Analytics

Independent work

Time management

Oral & written communication

Education

PhD in Computer Science or related

Job description

Heriot-Watt University invites a part-time Research Associate to contribute to a UKRI-funded project on Trustworthy AI for Peer Review. The role focuses on data processing, analysis of REF and publisher data, and testing the Trust Card framework to reveal uncertainty, disagreement and bias in scientometric indicators.

You will work with two other part-time colleagues to develop and validate the Trust Card and to assess novel indicators within an interdisciplinary environment.