PhD Position in Machine Learning-accelerated design of composite materials for hydrogen economy

PhD Position in Machine Learning-accelerated design of composite materials for hydrogen economy

Apr 9, 2024 Posted by:   webmaster No Comments

University of Warwick

Unlock a unique opportunity with a fully-funded PhD Project

Join us in a groundbreaking research endeavour aimed at revolutionising design of composite materials for cryogenic hydrogen applications in aerospace. We are seeking an enthusiastic student to lead a challenging PhD project focused on developing a multiscale methodology driven by Artificial Intelligence (AI) and mechanistic modelling across the scales to provide state-of-the-art design tools to optimise composite materials for problems associated with cryogenic hydrogen storage. This project is funded by the EPSRC HetSys II CDT at the University of Warwick, in partnership with the National Composites Centre (NCN).

The Challenge

Hydrogen fuel is envisaged as a key pillar in a future green economy with the potential to decarbonise aerospace industry. Efficient design of advanced composite materials that can sustain harsh cryogenic hydrogen applications is critical to achieving decarbonisation goals and paving the way for a more sustainable future. Multi-scale modelling methodologies that integrate modelling concepts from physics and engineering and are accelerated with AI/Machine Learning (ML), are crucial for paving the way for a more sustainable design of composite materials. This project will develop a radically-new predictive platform by leveraging cutting-edge techniques of mechanistic and AI-driven frameworks to pioneer new design ways for composites towards a greener future.

Why Warwick?

The University of Warwick has been awarded £11 million to train PhD students in computational modelling within the Centre for Doctoral Training in Modelling of Heterogeneous Systems, known as HetSys II. The new centre which spans seven departments and three university research centres will train 50 PhD students to use computational modelling to tackle pressing global sustainability challenges.

Who Should Apply?

We’re looking for candidates with a minimum of an upper second-class honours’ degree (or equivalent) in Engineering, Maths, and Physics – candidates with Chemistry background interested in multiscale materials modelling will also be considered. This exclusive opportunity is only open to UK residents. For more information and to apply, visit or contact Dr Lukasz Figiel (


• Fully funded PhD studentship at a leading academic institution.

• Advanced training in computational modelling within a multidisciplinary cohort

• Gain hands-on experience in cutting-edge AI-based multiscale modelling

• Contribute to groundbreaking collaborative research with real-world impact on sustainability and industry.

Postdoctoral Research Associate in Phase Field Modelling of Corrosion Fatigue

Mar 18, 2024 Posted by:   webmaster No Comments

University of Oxford – Department of Engineering Science


We are seeking a full-time Postdoctoral Research Associate in Phase Field Modeling of Corrosion Fatigue to join Prof. Emilio Martinez-Paneda’s research group (the Mechanics of Materials Lab) at the Department of Engineering Science (central Oxford).  This post is fixed-term for 12 months and funded by an EPSRC Supergen ORE Hub grant (CF-PREDICT) and the work will be conducted in close collaboration with the University of Strathclyde (Prof. Ali Mehmanparast).

You will run finite element simulations to understand material-environment interactions, with a particular interest in the structural integrity behaviour of offshore wind turbines. As offshore wind farms are reaching the end of their service life (25 years), there is growing interest in developing fundamental corrosion fatigue models that can assist in enabling life-extension decisions. Phase field models for corrosion, fatigue and fracture offer a unique opportunity to tackle this scientific and technological challenge. You will have the opportunity to use commercial finite element packages such as COMSOL or ABAQUS but also in-house or open-source codes such as FEniCS or MOOSE. You will also be expected to take part in collaborative work, take part in the activities of the research group, submit publications to reputed journals and liaise with academic collaborators and sponsors.

You must have obtained or be close to obtaining a PhD in engineering, mathematics, physics, materials science, or other closely-related disciplines. Also, you should have experience in at least one of the following: Finite element analysis, Multi-physics modelling , ABAQUS or COMSOL, Fracture Mechanics, Phase field models, Computational Mechanics

The initial appointment will be for one year but there are opportunities for extension based on performance and/or availability of funding.

Informal enquiries may be addressed to Prof. Emilio Martinez-Paneda

How to apply:

Candidates should submit a formal application, details of how to do so can be found here.

PhD Scholarship: Artificial Intelligence driven multi-physics phase field fracture simulations for composites

Mar 4, 2024 Posted by:   webmaster No Comments

University of Warwick – School of Engineering


Dr. Emmanouil Kakouris (Eng.), Dr. Lukasz Figiel (WMG)


Composites are widely adopted by automotive, aeronautical, and structural engineering due to their enhanced properties, yet their complex heterogeneous structure presents several challenges. Fracture is recognised as the main one, as it impacts composite safety, and when coupled with other physics, can lead to complex thermo-mechanical damage/failure scenarios. Commercially viable composite structures demand numerical methods adept at handling such complexities. This research aims to utilise the latest computational material modelling techniques to predict complex cracking patterns in composites, followed by creating an AI-driven multi-physics model for fast structural assessments. Outcomes will include enhanced understanding of damage processes, a new approach for investigating damage processes via phase-field fracture simulations, and a method to accelerate simulations using scientific machine learning.

How to apply:

Candidates should submit a formal application, details of how to do so can be found here:

Associate Professor in Computational Mechanics (Durham University, UK)

Dec 18, 2023 Posted by:   webmaster No Comments

Closing date 19 Feb 2024.

Department of Engineering

Grade 9: – £57,696 – £64,914 per annum

Open-Ended/Permanent – Full Time

Contract Duration: Open-Ended/Permanent

Link here

Applicants must demonstrate research excellence in the field of Computational Mechanics applied to problems in solid mechanics, with the ability to teach our students to an exceptional standard and to fully engage in the services, citizenship and values of the University. 
We welcome applicants with research and teaching interests in one or more of the following Computational Solid Mechanics application areas: fracture / fatigue, coupled problems, multi-physics problems, contact and friction, plus other areas that focus on solving / understanding solid mechanics problems using computational methods.  We are open to candidates that specialise in the development of new numerical methods as well as those that focus on applying existing techniques to solve challenging engineering problems.    

PhD Scholarship: Integrating machine learning and multiscale modelling for simulating fracture in materials with uncertainties

Dec 15, 2023 Posted by:   webmaster No Comments
University of Warwick – School of Engineering

Qualification: Doctor of Philosophy in Engineering (PhD)

Start date: 8th January 2024 or 1st April 2024 or 30th September 2024

Funding for: 3.5 years

Supervisor: Dr Emmanouil Kakouris and Dr Lukasz Figiel

Application deadline: The application deadline for this position is January 31, 2024. Prospective candidates are strongly encouraged to submit their applications at the earliest opportunity. The application process will be closed upon the identification of a suitable candidate.

Project Description:

Early detection of damage in materials is crucial, as cracks reduce local stiffness, can affect structural integrity, and accelerate the ageing process of physical assets. This project will help predict damage degradation in materials and enable mitigation measures to prevent potential failure of structural components, which are critical for ensuring safety and achieving societal objectives.

The aim of the project is to exploit the recent advances in machine learning (ML) and multiscale modelling for simulating damage in materials. With the increasing complexity of emergent materials, predictive structural damage models require mechanistic understanding across different material scales, i.e. multiscale modelling. This research will focus on modelling the link between the micro-material properties of engineering materials and their macroscale mechanical behaviour while retaining adequate precision and accuracy. ML tools will be utilised to pre-process massive amounts of data, integrate and analyse it from different input modalities and different levels of fidelity, identify correlations, and infer the non-linear response of the overall system. The project will focus on developing both deterministic and probabilistic frameworks to predict the response of structural components undergoing damage in real time. The probabilistic model will capture the uncertainties present in the data as well as in the ML-driven physics-based model. 
We are looking for candidates to work at the confluence of structural mechanics, uncertainty quantification, and ML, towards addressing the safety and resilience challenges of an ageing, growing, and changing critical infrastructure.

The successful candidate should have an interest in computational material modelling, simulations, machine learning, and mathematics for solving partial differential equations. The candidate should have good programming skills (any of the followings Python, MATLAB, C/C++, FORTRAN or others).

The successful applicant will be situated within the School of Engineering and is encouraged to initiate the program at their earliest convenience.


The award will cover the tuition fees at the UK rate £4,712, plus a tax-free stipend of £18,622 per annum for 3.5 years of full-time study. International candidates are welcome to apply but would be required to meet the fee difference.


UK candidates with a first-class or 2.1 honours degree at BSc or MSc in engineering disciplines, applied mathematics, physical science or computational science and a strong interest in computational materials modelling, simulations, applied mathematics and machine learning. International students are welcome to apply but must meet the fee difference themselves.

How to apply:

Candidates should submit a formal application, details of how to do so can be found here 

Application form ‘Course search’:

Department: School of Engineering

Academic Year: 2023/24

Type of Course: Postgraduate Research

  • Engineering (MPhil/PhD) (P-H1Q2)

In the application form funding section, enter: Source: EK-Early Detection Machine Learning

If you wish to discuss any details of the project informally, please contact Dr Emmanouil Kakouris at

Open call – Postdoc on computational modelling of hydrogen-assisted fractures at the University of Oxford

Nov 24, 2023 Posted by:   webmaster No Comments

Applicants are sought for a postdoc position to work on hydrogen embrittlement modelling. The postdoc will be based at the University of Oxford and supervised by Prof. Emilio Martínez-Pañeda. Salary: £36,024 – £44,263. The PDRA will have access to state-of-the-art HPC facilities and will also have the opportunity to (co-)supervise PhD and MSc Theses.

Closing date: 29 November 2023

Further information: