“Autonomous Formulation” refers to the use of autonomous software or robots to determine the composition and recipe of mass-market products (e.g. shampoo). Exploring the space of possible formulations is a complex problem that poses several challenges for material scientists; however this complexity can be offset by using computational experiments that can, to some extent, predict behaviours and characteristics. These experiments are typically workflow-centric and data-rich and pose new challenges to guarantee reproducibility. Indeed, typical data-driven tasks coordinate and orchestrate computational tasks according to a workflow description, which however is not sufficient to capture all the data and meta-data necessary for understanding the full context of an experiment. Ontologies have proved successful in representing, understanding, sharing and reusing research data, methods and metadata in computational experiments.
Recently, ontology-based abstractions (e.g. Research Objects) have been proposed to support the structuring, sharing, reusing and preservation of scientific assets and their annotations related to a computational experiment.
The objective of this PhD is to adapt and extend ontology-based abstractions to effectively model computational simulations of formulations. This architecture will allow the use of machine learning techniques to optimise specific formulation profiles with the aim to:
1) Reduce the time needed to set up and execute formulation experiments;
2) Support the autonomous determination of the formulations, thus increasing the number of experimental runs;
3) Preserve experimental configurations and the associated meta-data, therefore providing explicit benchmarking for new formulations.
The successful applicant will work as part of a team of computer scientists, roboticists, and computational chemists to achieve the project vision. The project is open-ended and concept driven.
This project is funded by EPSRC iCase award studentship and Unilever.
This project is only available for UK students or those with settled or pre-settled status in the UK.
4 years Full Time or part-time equivalent. Tuition fees are covered at the home rate (£ 4500 for the academic year 2021/22) and an annual stipend equivalent to current Research Council rates (£15,285 stipend for academic year 2021/22), plus support for travel/conferences/consumables.
International students should contact Dr Valentina Tamma in order to verify their eligibility criteria.
We are seeking creative and energetic individuals from a range of backgrounds. We require a 1st or 2:1 at first degree level and/or a Master’s degree level or equivalent to apply. Typical degree subjects include (but are not limited to) Computer Science, Chemistry, or Materials Science, particularly those with skills directly related to this project. We also welcome those who have significant relevant work experience.
Please provide the following information in your application
- Academic background – we are seeking creative and energetic individuals from a range of backgrounds. We require a 1st or 2:1 at first degree level and/or a Master’s degree level or equivalent to apply. Typical degree subjects include (but are not limited to) Computer Science, Chemistry, or Materials Science, particularly those with skills directly related to this project. We also welcome those who have significant relevant work experience.;
- A short statement on how your experience fits to the project to which you have applied, and how you would approach the project
- Write a short statement on why you would like to undertake PhD research in a multi-disciplinary cohort, and how you think the experience will benefit your career.
Please apply online here via the blue button ‘Ready to apply? Apply online.’
External Partner (Unilever)
- Unilever will support on project definition and steering, including industrial relevance
- The student will be expected to work for part of their study period at Unilever research labs based in the Liverpool University Campus, or in Port Sunlight, Wirral.
For any enquiries please contact Dr Valentina Tamma, Department of Computer Science, University of Liverpool on: [email protected]