Publisher
source

Dr T Hasell

1 year ago

Digital Discovery of Polymeric Materials University of Liverpool in United Kingdom

Degree Level

PhD

Field of study

Computer Science

Funding

Fully Funded

Deadline

Expired

Country flag

Country

United Kingdom

University

University of Liverpool

Suggested positions

Publisher
source

University of Liverpool

Country flag

United Kingdom

PhD opportunity at the University of Liverpool in Digital and Experimental Mechanochemistry for Next-Generation Elastomers . This project sits at the interface of polymer chemistry , polymer science , mechanochemistry , computational chemistry , and materials science . The research aims to build mechanistic understanding and predictive tools for designing high-performance elastomers for demanding industrial and technological applications. You will study how engineering elastomers degrade under e...

Computer Science
Chemistry
Chemical Engineering
+7 more

2 months ago

Publisher
source

University of Liverpool

Country flag

United Kingdom

PhD opportunity at the University of Liverpool in Discovery of Pb-free solders for use in the glass industry . This project sits at the intersection of chemistry , materials science , and computer science , with a strong emphasis on machine learning , computational chemistry , inorganic chemistry , and synthetic chemistry . Working with a major industrial partner, the successful candidate will build databases of alloy melting points, develop predictive ML models for Pb-free solder compositions, ...

Computer Science
Data Science
Chemistry
+8 more

1 month ago

Publisher
source

Argonne National Laboratory

Country flag

United States

Argonne National Laboratory is advertising a Postdoctoral Appointee in Materials Informatics and Autonomous Synthesis at the Center for Nanoscale Materials (CNM) in Lemont, Illinois, USA. The role sits at the intersection of materials science , chemistry , chemical engineering , polymer science , computer science , and data-driven experimentation, with a strong emphasis on AI/ML for autonomous materials discovery . The selected postdoc will develop machine learning-ready data resources and build...

Computer Science
Data Science
Chemistry
+6 more

1 week ago

Publisher
source

Kok Siew Ng

Brunel University London

Country flag

United Kingdom

Fully funded PhD opportunity at Brunel University of London within a NERC TREES Doctoral Landscape Award (DLA) for 2027. The project is titled A Systems Approach to Understanding the Environmental Fate and Impacts of Electronic Waste and focuses on the environmental fate and impacts of electronic waste, with strong links to Environmental Science , Chemical Engineering , Physics , Chemistry , and data-driven methods such as machine learning and data science . The PhD is interdisciplinary and incl...

Computer Science
Data Science
Chemistry
+7 more

2 weeks ago

Publisher
source

University of Southampton

Country flag

United Kingdom

PhD opportunity at the University of Southampton in predictive models for battery interfaces , combining quantum simulations , atomistic modelling , computational chemistry , machine learning , and electrochemistry . The project focuses on the solid electrolyte interphase (SEI) , a critical region in batteries that strongly affects performance and lifetime. You will work on large-scale quantum simulations using ONETEP together with reactive AI-driven force fields to build high-fidelity models of...

Computer Science
Chemistry
Chemical Engineering
+7 more

3 weeks ago

Keywords

Computer Science
Data Science
Machine Learning
Chemistry
Chemical Engineering
Materials Science
Computational Chemistry
Molecular Physics
Polymer Chemistry
Computational Physics
Automation
Physics

About this position

Computer aided design is used only rarely for polymeric materials, despite their importance across all technology sectors. Ultimately, this is because conventional simulations of polymers are tedious to set up, much slower than experimental synthesis and characterization, and therefore limited to the study of benchmark systems. This project will develop workflows for the accelerated and accurate simulations of thousands of polymers, opening the opportunity to use data analytics and machine learning to derive structure-property relations. It will become possible, for the first time, to establish a feedback loop between modelling and experiment for this class of materials, because the formulation of hypotheses through modelling will be faster than experiments. The methodology will be developed to be as general as possible, but a specific research problem will be tackled. We will consider polysulfide polymers produced by inverse vulcanization as an example of systems with defined but extremely large chemical space, and questions to answer. Models will be derived to describe the possible microscopic structure and will be used to interpret the data from experimental characterization, such as Raman spectroscopy. The project is suitable for students with background in Chemistry or Materials Science, with interest in the quantitative aspects of the disciplines and some previous experience with computational research or data analysis.

This project will be supervised by Prof. Alessandro Troisi and Dr. Tom Hasell (Dept. Chemistry). Any informal enquires about the project can be directed to .

The global need for researchers with capabilities in materials chemistry, digital intelligence and automation is intensifying because of the growing challenge posed by Net Zero and the need for high-performance materials across multiple sectors. The disruptive nature of recent advances in artificial intelligence (AI), robotics, and emerging quantum computing offers timely and exciting opportunities for PhD graduates with these skills to make a transformative impact on both R&D and society more broadly.

The University of Liverpool EPSRC Centre for Doctoral Training in Digital and Automated Materials Chemistry is therefore offering multiple studentships for students from backgrounds spanning the physical and computer sciences to start in October 2025. These students will develop core expertise in robotic, digital, chemical and physical thinking, which they will apply in their domain-specific research in materials design, discovery and processing. By working with each other and benefiting from a tailored training programme they will become both leaders and fully participating team players, aware of the best practices in inclusive and diverse R&D environments.

This training is based on our decade-long development of shared language and student supervision between the physical, engineering and computer sciences, and takes place in the Materials Innovation Factory ( MIF ), the largest industry-academia colocation in UK physical science. The training content has been co-developed with 35 industrial partners and is designed to generate flexible, employable, enterprising researchers who can communicate across domains.

Applicant Eligibility

Candidates will have, or be due to obtain, a Master’s Degree or equivalent related to Physical Science, Engineering or Computational Science. Exceptional candidates with a First Class Bachelor’s Degree in an appropriate field will also be considered.

Application Process

Applicants are advised to apply as soon as possible no later than 17th February 2025 . The CDT will hold two rounds of applications assessment:

  • Assessment Round 1 : for all applications received between 11th December 2024 – 15th January 2025.
  • Assessment Round 2 : for all applications received between 16th January 2025 – 17th February 2025

Applicants who wish to be considered in Assessment Round 1 must apply by 15th January 2025. Projects will be closed when suitable candidate has been identified (this could be before the 17th February 2025 deadline).

Please review our guide on “ How to Apply How to apply for a PhD | Postgraduate research | University of Liverpool carefully and complete the online postgraduate research application form to apply for this PhD project in Chemistry.

We strongly encourage candidates to get in touch with the supervisory team to get a better idea of the project.

We want all our Staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, if you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result.

Please ensure you include the project title and reference number CCPR142 for example when applying.

Funding details

Fully Funded

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts and confirm requirements with the university.

Professors

Department of Chemistry

University Name
Country flag

United Kingdom

Research Interests

Polymer Chemistry

Artificial Intelligence

Materials Science

Data Science

Chemical Engineering