Positions

Japan Agency for Marine-Earth Science and Technology

Aim of Position

In order to support and encourage talented early career researchers who have just obtained Ph.D. to further develop their research theme, JAMSTEC launched the program to foster them in 2014. This program renamed as “JAMSTEC Young Research Fellow (JYRF)” in 2019, has been producing excellent researchers who are active internationally.
JAMSTEC invites early career researchers who are expected to play an active role in the international community and who are willing to take on challenging research activities. This year there are about five JYRF positions to be filled.

Job Description

Successful fellows are expected to independently conduct scientific work on research topics of their own choice or to become involved in research already being conducted at JAMSTEC. 
Research grants will be provided to successful fellows to facilitate the smooth start-up of their research at JAMSTEC (1,000,000 for the first fiscal year and 500,000 for the second and third years each). The successful fellow will also have access to the necessary facilities and equipment in JAMSTEC during the contract period.
Each successful fellow will be assigned a mentor and a sub-mentor. They are responsible for providing advice on the successful fellow’s research progress and support for the all aspects of research life.

JAMSTEC’s Research and Development:
https://www.jamstec.go.jp/e/about/research/

Research Vessels, Facilities and Equipment:
https://www.jamstec.go.jp/e/about/equipment/

[Description of Duties]
(Immediately upon employment)
Successful fellows will be expected to engage in the research subject which is authorized by JAMSTEC in the assigned affiliation under direction of leaders.

(The scope of possible changes)
The duties designated by JAMSTEC

*The assignment to an institute will be determined by the screening committee.
*There may be a change in affiliation in the event of a reorganization of JAMSTEC.

[Relevant Research Fields]
Any natural science or engineering field

Requirements

Applicants are required to have a Ph.D. in natural science or engineering field (including applicants expected to obtain a Ph.D. by the effective date of employment.)
*Specific dates regarding eligibility are below;
・ Applicants who have already been awarded a Ph.D. on or after August 1, 2019
・ Applicants who are expected to obtain a Ph.D. by September 30, 2025

Closing Date

August 26, 2024 at 23:59 in Japan time 
*All the required documents, including the letter of recommendation, should be submitted from the website by this time and date.
*Depending on the situation of applications, the closing date may be extended.

More Information

For more information and to apply, visit https://www.jamstec.go.jp/e/work_with_us/jobs/details/jyrf20240826 or contact Dr Ettore Barbieri (e.barbieri@jamstec.go.jp).

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 https://warwick.ac.uk/fac/sci/hetsys/apply/ or contact Dr Lukasz Figiel (l.w.figiel@warwick.ac.uk).

Benefits

• 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.

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.