research assistant

University of Sheffield; Cheadle; On-site; GBP 32,000 - 34,000; Part time

Job summary

A UK research institution is seeking a part-time research assistant to support a project on NHS gambling treatment services. The role involves managing and analyzing health datasets, engaging with stakeholders, and ensuring data protection compliance. Ideal candidates should have a relevant undergraduate degree and experience with large health datasets. The position offers a salary of £32,080 to £33,951 per annum, depending on skills and experience.

Benefits

Competitive salary

University benefits including support for applicants with disabilities

Qualifications

• Undergraduate qualification in a relevant subject.
• Experience with complex clinical datasets.
• Excellent organisational skills.

Responsibilities

• Support research applying quantitative methods.
• Extract and manage information from healthcare records.
• Engage with stakeholders including clinicians.

Skills

Organisational skills

Working with large datasets

Data management

Education

BSc in Psychology, Computer Science, Data Science, or Health Informatics

MSc in Data Science or related field

Job description

Overview

As a research assistant, you will join a team working on the project 'Understanding and improving engagement and retention in NHS gambling treatment services', which is led by Professor Matt Field and funded by the Academic Forum for the Study of Gambling. The research assistant will provide specialist research support to the project, particularly extracting and coding information, merging different datasets, and securely managing data. You will work closely with other members of the project team and stakeholders including clinicians and people with lived experience of treatment. The overarching aim of the project is to use machine learning methods to understand why many people who are referred for treatment will drop out prematurely. To do this, two studies are planned. One will use a machine-learning-driven content analysis of referral notes and use this information, alongside contextual factors, to distinguish who is likely to attend their initial assessment versus who is not. The other study will use data from initial clinical assessments alongside contextual factors and treatment characteristics to identify characteristics of people who complete treatment and people who drop out prematurely, again using machine learning methods. Important outcomes from the project include developing and validating tools that can identify service users who require additional support or different forms of support to help them remain engaged with treatment. This tool can be applied in future work and can inform additional research that will develop and evaluate interventions to improve engagement and retention in treatment. The research assistant will play a key role in preparing and managing the datasets, including extracting, cleaning, merging, and coding data from clinical records, and supporting the development of initial analytic pipelines for supervised machine learning models.

Responsibilities

• Support research that applies advanced quantitative methods to identify predictors of engagement and retention in NHS gambling treatment services.
• Extract, code, merge and securely manage information derived from healthcare records.
• Contribute to research governance, including applications for ethical approval.
• Ensure data management complies with data protection legislation and information security requirements.
• Engage with stakeholders, including clinicians and people with lived experience of treatment for gambling disorder.
• Attend and contribute to project meetings and other relevant meetings in the School of Psychology and the University of Sheffield.
• Make ethical decisions in your role and embed the University sustainability strategy into your working activities wherever possible.
• Carry out other duties commensurate with the grade and remit of the post.

Person Specification

Essential criteria

• Undergraduate qualification in a relevant subject such as a BSc in Psychology, Computer Science, Data Science, Health Informatics or another health-related field (assessed at application).
• Experience working with large, complex multi-variable health or clinical datasets that include identifiable and sensitive information (assessed at application and interview).
• Excellent organisational skills, including ability to work to deadlines and manage your own workload (assessed at application and interview).

Desirable criteria

• Recent experience of working with NHS systems, including healthcare records.
• Postgraduate qualification in a relevant subject area, such as an MSc in Data Science, Health Data Analytics, Health Informatics or Computational Social Science.
• Experience with Natural Language Processing (NLP) or text mining, particularly for extracting information from free-text clinical notes, referral forms, or similar unstructured data.
• Knowledge of open research practices and willingness to engage with them (preregistration, preprints, data sharing). (Assessed at application and interview).
• Interest in and knowledge of gambling addiction and its treatment.

Job Details

• Grade: 6 (Salary: £32,080 to £33,951 per annum pro‑rata, dependent on skills and experience)
• Work arrangement: Part‑time (50% FTE)
• Duration: 6 months
• Line manager: Principal Investigator, Professor Matt Field
• Direct reports: Principal Investigator, Professor Matt Field

Next Steps

It is anticipated that the selection process will take place in early 2026. This will consist of an interview. We plan to let candidates know if they have progressed to the selection stage within four weeks of the closing date. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process, you can contact Professor Matt Field for assistance.

Contact

For informal enquiries about this job contact Professor Matt Field on [e-mail]. More details can be found on our benefits page: https://www.Sheffield.Ac.Uk/jobs/benefits