Research Assistant in Deep Learning-Coupled Finite Element Simulation
Develop cutting-edge computational tools integrating deep learning and finite element methods for fracture mechanics and solid mechanics.
Best for:Best for engineers or researchers with a strong background in mechanical/aerospace engineering, deep learning, and finite element methods, seeking hands-on experience in interdisciplinary computational research.
Overview
This one-year fixed-term Research Assistant position at Queen Mary University of London focuses on advancing computational simulation tools by combining deep learning with finite element methods. The project, funded by the UKRI EPSRC, collaborates with EPFL (Switzerland) and the University of Florida to address fracture mechanics and broader solid mechanics challenges. The successful candidate will work under Dr Sathiskumar Anusuya Ponnusami, contributing to interdisciplinary research, software development, and dissemination of findings in conferences and journals. The role bridges artificial intelligence and engineering, offering a dynamic environment for innovation in computational mechanics.
What you'll do
- Develop novel computational frameworks combining state-of-the-art deep learning models with finite element simulations for solid mechanics problems.
- Conduct research on fracture mechanics and related solid mechanics challenges using interdisciplinary AI and engineering approaches.
- Support research administration, including project documentation and reporting.
- Collaborate with international partners (EPFL and University of Florida) on research projects.
- Present research findings at conferences and publish results in academic journals.
- Contribute to software development for finite element analysis and machine learning applications.
Requirements
Degree required: Master’s degree in Mechanical or Aerospace Engineering, or a related discipline
Language: English; proficiency sufficient for academic research and collaboration
Instruction: English
- Proven experience with machine learning and deep learning methods applied to mechanics or engineering problems
- Strong understanding of the finite element method and its numerical implementation
- Proficiency in programming for computational research (e.g., Python, C++, MATLAB)
- Experience with research software development in finite element analysis
- No explicit nationality or residency restrictions mentioned
- No age or prior experience caps specified
Funding & terms
How to apply
Deadline: 2026-10-20
Applicants must submit their CV and a cover letter outlining their relevant experience and motivation through the external application portal. Ensure your application highlights your expertise in machine learning, finite element methods, and computational mechanics. The application process involves reviewing qualifications and experience against the role requirements.
- CV
- Cover letter
Create a free profile to unlock your personal match score, the full requirements, funding details and how to apply — in English or Persian.