Research Assistant in Digitisation of Manufacturing Processes

Job Description

The University of Sheffield is a remarkable place to work. Our people are at the heart of everything we do. Their diverse backgrounds, abilities and beliefs make Sheffield a world-class university.

We offer a fantastic range of benefits including a highly competitive annual leave entitlement (with the ability to purchase more), a generous pensions scheme, flexible working opportunities, a commitment to your development and wellbeing, a wide range of retail discounts, and much more. Find out more about our benefits (opens in a new window) and join us to become part of something special.

 

Overview

 

We are pleased to announce an exciting new opportunity to advance the state-of-the-art in digitisation of manufacturing processes. This position is focused on utilising various sensing technologies to generate in-process data that will be used to measure and monitor the manufacturing process parameters that are key to achieving successful outcomes. You will utilise knowledge of machine learning, deep learning, optimal control, and physics-based modelling.

 

You will have completed, or be close to completion of, an MSc or MEng in a subject related to sensor setup development, sensor data processing, or equivalent technical experience in the development of hardware/software integration. Ability to work effectively with both internal colleagues and external industrial partners would be essential. The ability to work in a multi-disciplinary team and to communicate research findings to a wide range of academic and industrial audiences is also essential for this role

 

The post will be based in the Digital Manufacturing Laboratory, led by Professor Ashutosh

Tiwari, within the School of Mechanical, Aerospace and Civil Engineering.

 

 

Main duties and responsibilities

  • Utilise in-process monitoring techniques employing a variety of sensing technologies to generate data that will be used to measure and monitor manufacturing process parameters.
  • Utilise data processing, fusion and analytics to identify links between process data obtained from sensors and the production outcomes.
  • Use existing control methods that can make use of the sensor data and the identified links to ensure successful production outcomes.
  • Collaborate with the partners involved within the project on use cases and demonstrator development, and test the developed system in a real manufacturing context.
  • Coordinate and liaise with other members of the research group over work progress.
  • Contribute to high quality reports and progress reports to contribute to academic publications.
  • Contribute to development of journal papers for high profile and respected conferences and journals at national/international level to disseminate research findings.
  • Carry out administrative roles as required such as coordinating meetings across various sites.
  • Participate in the general collaborative working of a team for example, to present to the group, participate in its seminar meetings, engage in its training events, and to demonstrate research to visitors etc.
  • As a member of staff you will be encouraged to make ethical decisions in your role, embedding 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

Our diverse community of staff and students recognises the unique abilities, backgrounds, and beliefs of all. We foster a culture where everyone feels they belong and is respected. Even if your experience doesn't match perfectly with this role's criteria, your contribution is valuable, and we encourage you to apply. Please ensure that you reference the application criteria in the application statement when you apply.

 

Essential criteria

 

  • Hold a Masters degree (or have equivalent experience) in a subject related to sensors, data processing, sensor data processing or machine learning for a manufacturing process. [Application]
  • Technical experience in the development of hardware / software integration for demonstrators. [Assessed at: Application/Interview]
  • Experience in the following skill areas:
  1. Operation of thermal crimping or similar welding  process

(2) Use of sensor technologies for real-time data capture during thermal crimping or similar welding process

  • Proficiency in any machine-learning-oriented programming languages (C/C++, Python, Java, C#, MATLAB, etc.) including relevant deep learning libraries (PyTorch, TensorFlow, Keras, etc.)
  • Experience of working on a research project that focuses on an industrial use case [Assessed at: Application]
  • Ability to work effectively as part of a multidisciplinary research team. [Assessed at: Interview]
  • Ability to assess and organise resources and plan and progress work activities to meet key project deadlines. [Assessed at: Interview]

 

Desirable criteria 

 

  • A track record of publishing high quality peer-reviewed articles.[Assessed at: Application/Interview]
  • Having the tenacity to solve challenging research problems, leveraging existing knowledge, learning new skills, and developing both innovative and pragmatic solutions. [Assessed at: Application/Interview]

 

Further Information

Grade: 6.1

Duration: Fixed-term until 2nd March 2026

Line manager: Professor Ash Tiwari, Project Lead

Our website: www.sheffield.ac.uk/mac

 

For informal enquiries about this job contact
Divya Tiwari - Lead Researcher FEMM Hub: on d.tiwari@sheffield.ac.uk

 

Next steps in the recruitment process

It is anticipated that the selection process will take place on  20th February 2025. This will consist of an interview and presentation. We plan to let candidates know if they have progressed to the selection stage on the week commencing 27th February 2025. If you need any support, equipment or adjustments to enable you to participate in any element of the recruitment process you can contact mac-recruiters@sheffield.ac.uk

 

Our vision and strategic plan

We are the University of Sheffield. This is our vision: sheffield.ac.uk/vision (opens in new window).
 

What we offer

  • A minimum of 38 days annual leave including bank holiday and closure days (pro rata) with the ability to purchase more.
  • Flexible working opportunities, including hybrid working for some roles.
  • Generous pension scheme.
  • A wide range of discounts and rewards on shopping, eating out and travel.
  • A variety of staff networks, providing opportunities for social interaction, peer support and personal development (for example, Race Equality, LGBT+, Women's and Parent's networks).
  • Recognition Awards to reward staff who go above and beyond in their role.
  • A commitment to your development access to learning and mentoring schemes; integrated with our Academic Career Pathways / Technical Career Route
  • A range of generous family-friendly policies
    • paid time off for parenting and caring emergencies
    • support for those going through the menopause
    • paid time off and support for fertility treatment
    • and more


More details can be found on our benefits page: sheffield.ac.uk/jobs/benefits (opens in a new window).

 

We are a Disability Confident Employer. If you have a disability and meet the essential criteria for this job you will be invited to take part in the next stage of the selection process.

Closing Date : 12/02/2025 

 

We are a research university with a global reputation for excellence. Our ideas and expertise change the world for the better, making a real difference to society. We know that when people come together with different views, approaches and insights it can lead to richer, more creative and innovative teaching and research and the highest levels of student experience. Our University Vision ( www.sheffield.ac.uk/vision ) outlines our commitment to building a diverse community of staff and students that recognises and values the abilities, backgrounds, beliefs and ways of living for everyone.