Research Associate - A Novel Scientometric Indicator for Detecting Scientific Fraud through Dark Data Validation (SATERN)

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

Benefits offered by this job

Flexible working patterns

Job summary

Heriot-Watt University seeks a Research Associate to contribute to the SATERN project, developing novel scientometric indicators for detecting scientific fraud. You will work on image forensics, multimodal ML, and clustering, collaborating with Strathclyde, Surrey, and industry to advance fraud detection in scholarly publishing.

The role requires a PhD in a relevant field, strong Python skills, and the ability to travel for project meetings within the UK.

Qualifications

• PhD in computer science or close to completion.
• Evidence of a developing publication record in AI related research.
• Experience in machine learning research and applications.
• Very good programming skills in Python.
• Evidence of self-motivation and the ability to work independently.
• Evidence of excellent oral and written communication skills.
• Excellent time management skills including the ability to prioritise workload.
• Ability to travel to project meetings within UK and international conferences as requested by the project.

Responsibilities

• Conduct and disseminate original research aligned with the project and in collaboration with the team.
• Develop multimodal machine learning models to detect paper fraudulence by working closely with team members and project partners.
• Apply clustering techniques to detect image reuse networks and paper mills.
• Collect and preprocess multimodal data from a large number of papers.
• Pilot, dissemination, and commercialisation of the indicator.
• Collaborate to organise events and support project meetings across the UK.
• Identify sources of funding and help prepare research proposals for funding bodies

Skills

PhD in computer science

AI/ML research

Python programming

Interdisciplinary collaboration

Self-motivation

Oral and written communication

Time management

Travel to meetings

Education

PhD in Computer Science

Tools

Python

Job description

Role Title: Research Associate - A Novel Scientometric Indicator for Detecting Scientific Fraud through Dark Data Validation (SATERN)

Grade and Salary: Grade 7, £37,694 - £47,389 per annum

FTE and working pattern: Full-time (35hrs per week) Fixed Term (12 Months)

Reward and Benefits: 33 days annual leave, plus 9 buildings closed days for all full time staff. Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewards-calculator.htm to see the value of benefits provided by Heriot-Watt University.

Purpose of Role

We are recruiting a full time research associate to contribute to the principally funded UKRI project named “A Novel Scientometric Indicator for Detecting Scientific Fraud through Dark Data Validation (SATERN)”.

The project aims to deliver a novel scientometric indicator that identifies both single modality and cross-modal fraudulence in published papers. SATERN will detect fraud by extracting hidden numerical data from scientific images/figures; what we call dark data; and cross-validating it against other modalities such as texts and tables. our indicator triangulates across all three modalities simultaneously because it is harder to fraudulently manipulate all modalities consistently. The developed indicator will serve editors, funders, institutions, and publishers, enabling evidence-based decisions to reject fraudulent submissions and screen published papers at scale.

The successful candidate will contribute to develop novel and apply existing image forensics techniques to detect image manipulation and apply clustering techniques to detect image reuse network and paper mills. The candidate will also help develop multimodal machine learning models to detect paper fraudulence from multimodal data (images, texts, and tables) in collaboration with researchers from Strathclyde, Surrey, and industry.

Key Duties and Responsibilities

• Conduct and disseminate original research aligned with the project and in collaboration with the team.

• Responsible for developing multimodal machine learning models to detect paper fraudulence by working closely with team members and project partners.

• Responsible for applying clustering techniques to detect image reuse networks and paper mills.

• Contribute to collecting and preprocessing multimodal data from a large number of papers.

• Contribute to pilot, dissemination, and commercialisation of the indicator.

• Work as part of the team to help organize events and support the work of the team as needed, including travelling to project meetings within UK.

• Identify appropriate sources of funding and help prepare research proposals for funding bodies

• Demonstrate initiative in making routine decisions in order to manage and achieve project deliverables and deadlines

• Participate in, and develop, networks and collaborations both internally and externally to the Division/School /University

• Any other duties, commensurate with the grade of the post

Person Specification

These are the criteria on which the short-listing and recruitment selection will be made.

Essential

1. PhD in computer science or relevant discipline (or close to completion).

2. Evidence of a developing publication record in AI related research.

3. Experience in machine learning research and applications.

4. Very good programming skills in Python

5. Experience or desire to work with those from other disciplines

6. Evidence of self-motivation and the ability to work independently

7. Evidence of excellent oral and written communication skills

8. Excellent time management skills including the ability to prioritise workload

9. Ability to travel to project meetings within UK and international conferences as requested by the project.

Desirable

1. Experience in image analysis and clustering analysis

2. Experience in trustworthy AI and multimodal machine learning

3. Experience or knowledge in anomaly detection or fraudulence detection.

4. Experience in working in interdisciplinary research projects

5. Experience in working in externally funded research projects

6. Adaptability, willingness to learn new fields and develop new skill sets.

About Heriot-Watt University

At Heriot Watt we are passionate about our values and look to them to connect our people globally and to help us collaborate and celebrate our success through working together. Our research programmes can deliver real world impact which is achieved through the diversity of our international community and the recognition of creative talent that connects our global team.

Our flourishing community will give you the freedom to challenge and to bring your enterprising mind and to help our partners with solutions that can be applied now and in the future. Join us and Heriot Watt will provide you with a platform to thrive and work in a way that also helps you live your life in balance with well-being and inclusiveness at the heart of our global community.

Heriot-Watt University is committed to securing equality of opportunity in employment and to the creation of an environment in which individuals are selected, trained, promoted, appraised and otherwise treated on the sole basis of their relevant merits and abilities.Equality and diversity are all about maximising potential and creating a culture of inclusion for all.

Heriot-Watt University values diversity across our University community and welcomes applications from all sectors of society, particularly from underrepresented groups. For more information, please see our website https://www.hw.ac.uk/uk/services/equality-diversity.htm and also our award-winning work in Disability Inclusive Science Careers https://disc.hw.ac.uk/.

We welcome and will consider flexible working patterns e.g. part-time working and job share options.

Use our total rewards calculator: https://www.hw.ac.uk/about/work/total-rewards-calculator.htm to see the value of benefits provided by Heriot-Watt University.