Description

We are seeking a full-time Postdoctoral Research Associate in Computational Solid Mechanics to join Prof. Emilio Martinez-Paneda’s research group (the Mechanics of Materials Lab) at the Department of Engineering Science (central Oxford).  This fixed-term post is funded by an UKRI Future Leaders Fellowship grant (NEWPHASE).  This post is available for 12 months in the first instance.

We are looking for a researcher with interesting in running finite element simulations to understand inter-disciplinary mechanics topics spanning the areas of biology, materials science and glaciology, with a particular emphasis on coupled multi-physics problems. Examples include the growth of crevasses in large ice-sheets, the modelling of neurogenerative diseases, the degradation of Li-Ion batteries, and the corrosion and hydrogen embrittlement of metallic materials. There is certain flexibility and the tasks can be tailored the strengths and interests of the applicant. 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

Informal enquiries may be addressed to Prof. Emilio Martinez-Paneda  (email: )

For more information about working at the Department, see

www.eng.ox.ac.uk/about/work-with-us/

Only online applications received before midday on Monday 2nd December 2024 can be considered. You will be required to upload a covering letter/supporting statement, including a brief statement of research interests (describing how past experience and future plans fit with the advertised position), CV and the details of two referees as part of your online application.

For those candidates about to finish their PhD degrees, a lower grade offer may be made (Grade 6: £34,982 – £40,855 p.a.) with a commensurate reduction in responsibilities (and amendment in job title to Research Assistant) until the PhD is awarded .

The Department holds an Athena Swan Bronze award, highlighting its commitment to promoting women in Science, Engineering and Technology.

Keywords – Computational Solid Mechanics, Finite Element Analysis, Numerical Analysis, Multi-Physics Modelling, Phase Field Methods

Salary

Research Grade 7: £38,674 – £46,913 per annum

Closing date

Dec 02, 2024 12:00PM

Contact name: Emilio Martinez-Paneda

Contact email:

2 Postdocs position on physics-informed AI
 
In the context of the ESI Group-UZ Chair of the Spanish National Strategy on AI, we are looking for two highly skilled postdoctoral researchers to join the chair in the following two topics:

AI-powered mechanics of materials

One of the challenges of nowadays industry is to be able to accurately introduce the observed behavior of complex materials into high-fidelity simulations. In this framework, plasticity and damage play a fundamental role. Our thermodynamics-informed neural networks are an appealing choice for the incorporation of theoretical inductive biases to the learning process of complex constitutive equations including plasticity and damage.

Accelerating simulation by Artificial Intelligence

One of the biggest appeals of physics-informed neural networks relies in the possibility of developing learned simulators. Learned simulators are particular instances of simulation systems that are constructed not by discretization of the governing PDE—which may even be unknown—but by learning from data.
In this project, the objective is primarily to speedup complex parametric simulations. The typical example of such a simulation is a detailed crash test.

The successful candidates will join a dynamical and interdisciplinary team with a world-class network of collaborators towards:
• Creating the next-generation physics-informed AI techniques
• Develop advanced numerical methods and algorithms required to address cutting-edge scientific multidisciplinary problems
• Deploy state-of-the-art, open-source software that will be used on a daily basis by a wide range of researchers and industries around the world to advance science.

Required Qualifications

• Strong skills in phyton programming, preferably with a strong background in PyTorch
• Knowledge and previous experience in the development of physics-informed AI
• Ph.D. in computer science, computational mathematics, computational physics, or computational engineering. 
• Experience and understanding of numerical methods for partial differential equations.
• Enthusiasm for programming, learning, problem-solving, and tracking other library trends
• Exceptional collaboration and communication skills, in particular in English
• Strong background in computational solid and fluid mechanics
• Experience in finite element programming

You will have access to state-of-the-art computing facilities, a generous travel allowance for conferences and specialized training, and will be given the freedom to develop your own ideas and collaborate within the group and with other groups. These positions are designed so as to allow you to grow your abilities as a research advisor, by directing and collaborating with our doctoral students, under the supervision of profs. Cueto, González and Alfaro.

Besides Skype interviews, it is possible to arrange on-site visits to the lab and the campus in Zaragoza. 

The preferred starting date is June 2024 (or as soon as possible). We offer a two-year contract.
Interested applicants are encouraged to send a CV as soon as possible with a list of up to 3 references and a very concise statement describing your past research/technical experience and your interests. This and specific inquiries should be addressed to Elías Cueto ().