Doctoral Student in Neuronics
Develop AI-driven finite element human body models for injury biomechanics and traffic safety.
Best for:Recent MSc graduates in mechanical, biomedical or engineering physics with strong FEM, Python/Matlab and AI/ML experience.
Overview
KTH Royal Institute of Technology invites a highly motivated doctoral student to join the Neuronics project, which aims to create an AI‑driven platform for personalising and positioning finite element Human Body Models. The work will combine mechanics, computational modelling and machine learning to improve vehicle safety assessments and reconstruct real‑world accidents from video data. Supervision is provided by Xiaogai Li, Svein Kleiven and Shiyang Meng, in close collaboration with the industry partner Autoliv. The successful candidate will be expected to develop algorithms, establish quality criteria for model harmonisation, and publish research findings while completing a four‑year PhD programme.
What you'll do
- Design and implement an AI‑based platform to personalise finite element human body models.
- Develop algorithms for automatic positioning of models in realistic occupant postures.
- Create data‑driven, PCA‑based methods and quality criteria for model harmonisation across HBM families.
- Reconstruct real‑world traffic accidents from image and video data using the developed tools.
- Collaborate with the industry partner Autoliv and publish research results in peer‑reviewed venues.
- Contribute to teaching, training and limited administrative tasks as required by the doctoral contract.
Requirements
Degree required: MSc in Mechanical Engineering, Biomedical Engineering, Engineering Mechanics, Engineering Physics or equivalent
Degree offered: PhD
Language: English (equivalent to Swedish B/6)
Instruction: English
- Strong background in mechanics, computational mechanics or biomechanics
- Solid knowledge of continuum mechanics and the Finite Element Method
- Proficient programming skills in Python or Matlab
- Experience with AI/ML techniques such as deep learning or graph neural networks
- Ability to work independently and collaborate in a research team
- English proficiency equivalent to Swedish B/6
- Must have completed a second‑cycle degree (MSc) or an equivalent qualification
- Must satisfy the Swedish Higher Education Ordinance eligibility criteria
- Admission to postgraduate education at KTH is required
- Potential security clearance if the position is classified as security‑sensitive
Funding & terms
How to apply
Deadline: 2026-10-01 (Europe/Stockholm)
Submit your application through KTH's recruitment system before the deadline. Ensure that all required documents are uploaded and that your English proficiency certificate is included. The application will be reviewed for completeness and eligibility.
- Diplomas and transcripts (certified copies)
- Curriculum vitae
- Application letter (max 2 pages)
- Proof of English proficiency
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