Positions

University of Warwick – School of Engineering

Supervisors:

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

Summary:

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: https://warwick.ac.uk/fac/sci/hetsys/themes/projectopportunities/hp2024-02

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.    

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.

Scholarship:

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.

Eligibility:

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 https://warwick.ac.uk/fac/sci/eng/postgraduate/applypgr/ 

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 Emmanouil.Kakouris@warwick.ac.uk.

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: https://eng.ox.ac.uk/jobs/job-detail/?vacancyID=169307

The (part-time, 30hrs per week, for a duration of 3 years, starting as soon as possible) position is partly funded by the Linz Institute of Technology SeedMedPlus 2022 call as part of the project Next Generation Veno-Venous ECMO Via Flow Optimization, led by Prof. Dr. Luca Gerardo-Giorda from the Institute for Mathematical Methods in Medicine and Data-Based Modeling (M3DM) and by Prof. Dr. Jens Meier from the Department of Anaestesiology and Intensive Care Medicine (AICM) of the Kepler University Klinikum.

The PhD student will join M3DM and collaborate with AICM and the Mathematical Methods in Medicine and Life Sciences Group at RICAM, the Johann Radon Institute for Computational and Applied Mathematics of the Austrian Academy of Sciences (ÖAW). The goal of the project is the development of patient specific simulations to optimize the efficacy of Extra- Corporeal Membrane Oxygenation (ECMO), a well-established procedure used in Intensive Care Units (ICU) to treat patients with either pulmonary or circulatory failure.

Our offer:

• Close mentorship: the student will receive mentorship from both principal investigators, who foster healthy mentor-mentee relationships.
• The opportunity to work in an international and lively research environment, collaborate with experts in the field and medical doctors.
• A working time of 30h/week and a corresponding annual gross salary of € 34.411,72 according to the salary scheme of the Johannes Kepler University (JKU).
• German language is not required for the job, and we highly welcome international applications. Linz offers a lively and diverse atmosphere and Vienna is a little over 1h by train.

Your profile:

• Master’s degree in Applied Mathematics, Engineering, Computer Science or similar.
• Experience with C++ and Python programming languages.
• A keen interest in medical applications.
• Proficiency in English.
• Strong background in Finite Element Method (desirable).

Scientific inquiries about the position can be directed to Prof. Dr. Luca Gerardo-Giorda (luca.gerardo-giorda@jku.at).

Applications containing a scientific CV, a short research statement, and references for possible recommendation letters should be sent via email to cornelia.strasser@jku.at indicating in the subject “PhD position application: ECMO”. The position will be open until adequately filled.

The Johannes Kepler University pursues a non-discriminatory employment policy and values equal opportunities, as well as diversity. Individuals from underrepresented groups are particularly encouraged to apply.

Project description: 

There is a wealth of Industrial applications which require the use of computer models to understand the nature and behaviour of shock waves in solids caused by intense short-duration disturbances, such as sudden contact-impact loading and/or when a material is exposed to fast temperature changes. For instance, the mechanisms of material failure under a ballistic-shock are crucial in the design of 3D printed elastomer multi-materials, which are of interest to UKAWE.

Current shock-physics simulation codes, which largely rely on industry-optimised Computational Fluid Dynamics (CFD)-based methods, are capable of handling shocks across the entire domain of CFD applications. However, the success of this methodological approach has not been replicated in other fields of science, such as large strain solid dynamics, and its coupling with other physics and with novel materials.

Building upon very recent work made by the supervisory team, this project will investigate a new computational framework using a novel Arbitrary Lagrangian Eulerian (ALE) formalism written in the form of a system of first-order conservation laws. The resulting conservation-type ALE formulation, which displays striking similarities to that used by the CFD community, has inspired the investigators to adopt conventional CFD algorithms in the novel context of Computational Solid Dynamics. One key contribution of this work is that the physical deformation gradient can be obtained via its multiplicative decomposition into two auxiliary deformation gradient tensors, both computed via additional first-order conservation laws. Crucially, the new ALE conservative formulation will be shown to degenerate into Lagrangian and Eulerian mixed based systems of conservation laws. A Finite Volume/Discontinuous Galerkin/Smooth Particle Hydrodynamics algorithm with Riemann based upwinding stabilisation will be used for the spatial discretisation, exploiting in-house software.

The recruited PhD candidate will become a member of an active research group working on the development and application of cutting edge computational techniques for large strain solid dynamics, dynamic fracture/contact  and computational multi-physics.

Closing date: 24 July 2023

Further information: https://www.swansea.ac.uk/postgraduate/scholarships/research/civil-engineering-ukawe-su-phd-multimaterial-2022-rs380.php