Research Fellow - University of Warwick, United Kingdom - University of Warwick

University of Warwick
University of Warwick
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University of Warwick, United Kingdom

1 week ago

Tom O´Connor

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Tom O´Connor

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Description
Fixed Term Contract (Ending 31/03/2024), 36.5 hours per week (1.0 FTE)

The Visualisation Group at WMG are looking for a talented and highly motivated researcher to further our research on the development of machine learning methods on the development of sustainable polymer/composite manufacturing solutions in collaboration with the WMG National Polymer Processing Centre and selected industry partners.


  • A solid foundation in deep learning methods including the computing and statistical principles that found it.
  • Experience of working with pyTorch (or similar) for solving realworld problems or advancing machine learning solutions.
  • The ability to process and handle data collected from diverse sensors.
  • A publication records that demonstrates the above.
Experience in machine learning projects in a collaborative R&D environment (particularly in collaboration with industry) would be advantageous.

This exciting role will involve close collaboration with other academics and industrial partners and there will be opportunities for training and career development.

We will provide a great range of benefits which include an attractive pension scheme, 30 days holiday plus bank holidays and Christmas closure, some fantastic savings on a wide range of products and services, and excellent learning and development opportunities.


If you have not yet been awarded your PhD but are near submission or have recently submitted your PhD, any offers of employment will be made as Research Assistant on level 5 of the University grade structure (£31,411).

Upon successful award of your PhD and evidence of this fact, you will be promoted to Research Fellow on the first point of the level 6 of the University grade structure (£32,348).

Interview Date - TBC


Job Purpose


You will undertake the design and development of machine learning solutions on the HVM Catapult funded, data-driven recycling of plastics project.

Support the work of the department and develop and enhance its reputation, both internally and externally. Assist the project leader and co-investigator and where appropriate, project collaborators in the successful execution of the project.


Project Specific Responsibilities

  • To form an understanding of the current solutions for using data science and machine learning methods for identifying plastics.
  • To analyse and process data that has been captured (by other researchers on the project) from a variety of sensors.
  • To design and develop novel machine learning solutions.
  • To publish methods and results in relevant fora.

Principal Accountabilities

Research and Scholarship

  • Help establish a sound research base within the department in order to assist the development of research objectives and proposals for own or joint research.
  • Conduct individual and collaborative research projects.
  • Write up research work for publication.
  • May present information on research progress and outcomes to bodies supervising research, e.g. steering groups.
  • May contribute to the preparation of papers for steering groups and other bodies.
  • Communicate complex information (orally and in writing) and material of a specialist or highly technical nature.
  • Continually update own knowledge and understanding in field or specialism.

Teaching and Learning Support

  • Could be expected to contribute to the teaching and learning programmes in the department.
  • Assist in the supervision of student projects and the development of student research skills.
  • May be involved in the assessment of student knowledge and supervision of projects.

Administration and Other Activities

  • May be required to attend departmental meetings and to participate (where necessary) in other committees and working groups within the department, the faculty and the University.
  • Ensure compliance with health and safety in all aspects of work.
  • Work within budget constraints.
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