This is an exciting opportunity to gain experience in the manufacturing sector, conduct interesting analysis of quantitative data and conduct user research with customers of a CNC machine monitoring company based in Sheffield. Techniques involved: data science, user research, quantitative data analysis, statistical methods, machine learning
Pay Rate: £21.25 per hour + holiday pay entitlement
Location: The Wave, School of Information, Journalism and Communication
Hours per week: 20
Total hours: 400
Duration: 17th November 2025 – 28th February 2026
Job Description
- Study a dataset on current and energy drawn from spindles/tools in CNC machines to understand loading times and cycle times. The analysis will involve understanding distributions, conducting exploratory and statistical analysis of the features of the data and machine learning models (if deemed relevant)
- Engage with clients and the machine monitoring company to understand the production process and the different stages involved in machining
- Use user research methods to interview clients/the company to understand the manufacturing process and key performance indicators
- Develop metrics for job benchmarking using statistical and ML methods, and develop processes for operationalisation of benchmarks
Person Specification
- Candidates must be completing or close to completion of their PhD studies, with considerable experience of quantitative methods (statistical analysis - essential) and machine learning (desirable)
- Experience with user research methods – e.g. requirements gathering and analysis, interviews, focus groups, observational studies (essential)
- Experience with large temporal datasets (essential), particularly in manufacturing sector (desirable)
- Strong expertise in reading and writing academic reports (essential)
- Good communication and presentation skills (essential)
- Strong organisational skills and ability to manage time appropriately (essential)
Deliverables for the project are:
- A report that
- presents a state of the art (literature review) on metrics used for assessing job performance in manufacturing processes and methodologies used to analyse temporal data for performance assessment in machining;
- documents the research conducted in the project, analysis of the dataset and results from user studies;
- Write up of the job benchmarking metrics and strategies to develop the metrics
- Notebooks that will compute metrics, and visualisations of distributions of the metrics
To Apply: Please complete your application through myJobshop through the 'Apply' button. You will be asked to upload:
- Your CV
- An example of an academic report that you have written which can demonstrate your ability in conducting literature reviews, statistical analysis and/or machine learning and synthesising research
You will also be asked to complete 3 application questions on myJobshop.