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KTH Royal Institute of Technology

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Doctoral student in Neuronics KTH Royal Institute of Technology in Sweden

Degree Level

PhD

Field of study

Computer Science

Funding

Available

Deadline

Oct 1, 2026

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Country

Sweden

University

KTH Royal Institute of Technology

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Keywords

Computer Science
Biomedical Engineering
Mechanical Engineering
Deep Learning
Artificial Intelligence
Python Programming
Traffic Safety
Computational Mechanics
Posture
Biomechanic
Mechanic
ML

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About this position

Doctoral student in Neuronics at KTH Royal Institute of Technology, Sweden.

This PhD project focuses on AI-driven finite element human body modelling and injury biomechanics for improved traffic safety. The work aims to develop a platform that can personalize finite element Human Body Models (HBMs) and place them automatically in realistic occupant postures. A key application is the transition toward HBM-based vehicle safety assessment for consumer ratings and future regulations, where efficient and accurate personalization and positioning methods are needed for industrial deployment.

The project also supports systematic reconstruction of real-world accidents from image and video data. Methodologically, it combines data-driven approaches, PCA-based methods, biomechanics, computational mechanics, and AI/machine learning. The technical goal is to establish evidence-based quality criteria for harmonization and robust use across different HBM families.

The project is funded by Vinnova under the FFI (Strategic Vehicle Research and Innovation) programme and is carried out in collaboration with Autoliv. Supervision is proposed by Xiaogai Li, Svein Kleiven, and Shiyang Meng.

Eligibility highlights include a relevant background in mechanics, computational mechanics, or biomechanics, plus strong programming skills or experience in AI/machine learning. Preferred degrees include Mechanical Engineering, Engineering Mechanics, Biomedical Engineering, or Engineering Physics. Applicants must also meet KTH’s postgraduate admission requirements, including an equivalent master’s-level qualification and English proficiency equivalent to English B/6.

The position is a full-time temporary doctoral employment in Stockholm, Sweden, with a monthly salary according to KTH’s doctoral student salary agreement. The start date is 2026-12-01 or according to agreement. Applications are due by 2026-10-01 at 23:59 CET/CEST and must be submitted through KTH’s recruitment system.

Funding details

Available

What's required

Applicants must have basic eligibility for postgraduate education: a second-cycle degree such as a master’s degree, or at least 240 higher education credits with at least 60 second-cycle credits, or equivalent knowledge. The preferred candidate is a recently graduated Master of Science in Engineering, ideally with a master’s degree in Mechanical Engineering, Engineering Mechanics, Biomedical Engineering, Engineering Physics, or equivalent. A solid background in mechanics, computational mechanics and/or biomechanics is expected, along with strong knowledge of continuum mechanics and the Finite Element Method (FEM). Strong programming skills (e.g. Python, Matlab) and experience with AI/machine learning (e.g. deep learning, graph neural networks) are highly meriting. English proficiency equivalent to English B/6 is mandatory, and willingness to travel to research and industry partners is requested.

How to apply

Apply through KTH’s recruitment system and admission portal. Include diplomas and grades, proof of language requirements, a CV, and a motivation letter of up to 2 pages. Ensure all documents are certified and translated into English or Swedish if needed.

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