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Research Assistant – Machine Learning & Mobile App Development

Details


1104375


University of Gloucestershire


09/11/2025


9 Months


7 hours per week, working pattern to be confirmed


Smart Casual

Pay


£16.63


£2.01

Description

Role

We are seeking a self-motivated Research Assistant to join our dynamic team working on a cutting-edge project involving machine learning, computer vision, and mobile app development. The successful candidate will contribute to the development of a YOLOv8-based image recognition model, AI dashboard, a mobile application, data visualization. You will collaborate closely with a team of researchers, including external partners, to deliver impactful outputs within tight timelines.

About the Fast-Litter Project: The University of Gloucestershire research project is delivered through co-production with a borough council and Litter Community Group. 

It will produce:

  • A real-time data dashboard for councils to visualise litter hotspots and trends that inform action and policy.
  • A public-facing mobile app designed to capture geo-tagged, timestamped images of litter, collect data, and gamify activism.
  • An open-weight and open-source AI image recognition (YOLOv8) model to identify litter types, material, and brands from photos.

Duties and responsibilities

  • Develop and implement machine learning models for image recognition.
  • Develop mobile app projects involving image processing.
  • Create clear and insightful web-based data visualizations.
  • Work collaboratively with internal and external research partners.
  • Meet project deadlines and report on progress.

Skills and experience
Essential Requirements:

  • Strong skills in image recognition/classification AI models. 
  • Strong Python skills.
  • Android or iOS App development.
  • Experience with mobile application development, web development.
  • Self-motivated, able to work independently, and manage tight deadlines. 

Desirable:

  • Previous experience as a Research Assistant. 
  • Interest in research and its practical applications. 
  • MSc or PhD in a relevant field. 
  • Relevant prior work experience.

Location
Hybrid, to be confirmed

Additional information
Proposed Interview date: November to be confirmed to shortlisted candidates.

 
We ideally want the essential requirements to be met due to the nature of the role.

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