DC 2 – Mechanisms and Prevention for the Ageing Brain

1. Overview of the research programme:

InSilicoHealth is an innovative Doctoral Network (DN) with the ambition to train a new generation of outstanding
Doctoral Candidates (DC) that will become effective translators of the rapidly evolving digital technology to tackle
existing and future challenges related with healthy ageing in Europe. The research focus of this DN lies in three
key domains: the brain, heart, and musculoskeletal (MSK) systems. In the realm of digital technology, InSilicoHealth
specifically focuses on virtual human twin (VHT) technology to enhance our understanding of the age-related adaptive
changes of the complex human body through predictive multi-scale simulations. The research methodology employs
knowledge-driven models enhanced by advanced data-driven inference techniques to optimize the health potential of
older individuals.

2. Individual PhD Project Information:

Host institution
KTH Royal Institute of Technology (KTH), Sweden
Supervisory team
Prof. Svein Kleiven (PhD supervisor, KTH), Prof. Silvia Budday (PhD co-supervisor, FAU), Dr Xiaogai Li (PhD
co-cupervisor, KTH), Dr Madelen Fahlstedt (secondment host, Mips).
Enrolment in Doctoral School
School of Technology and Health (KTH) and the Department of Mechanical Engineering (FAU).

3. PhD project description:

This PhD project will focus on establishing head injury prevention tools for the elderly population to decipher the
geriatric injury mechanics and develop age-adapted protection solutions. Given the over-representation of the
elderly in head injury statistics, FE head models specifically for the active ageing need to be developed urgently
for improved and age adapted protection. This project will create individual specific injury prediction models that
can be used for safer interpretation of the mechanisms associated with trauma of elderly and ultimately lead to
improved head protection for the most vulnerable members of our society.
The doctoral student is offered a 4-year highly specialized doctoral training, making the candidate an expert in
head impact biomechanics and helmet design technologies as well as being aware of commercializable market
opportunities. The doctoral candidate will work in world-class facilities with highly qualified experts, and will
benefit from the training scheme developed based on the expertise of academic and industrial partners.
A successful project will improve our understanding on the mechanisms of geriatric head trauma, novel Finite Element
models of the ageing brain incorporating human properties, a first-of-its-kind detailed injury assessment tool for
the elderly population directly applicable for product development of protective equipment, and a curated large
dataset of injury mechanisms for an elderly population.

4. Planned secondments:

  • FAU: Aims to gain knowledge in the generation of person-specific finite element models of the ageing brain.
  • Mips: Opportunity for the DC to gain experience in product development in the industry, using an evidence-based
    approach, specifically for protective equipment.

5. Essential requirements:

  • You have completed a master’s degree in Mechanical Engineering, Biomedical Engineering, Engineering Physics or
    equivalent or possess corresponding qualifications that could provide a basis for successfully completing a
    doctorate.
  • You have a keen interest in either biomechanics, continuum mechanics or FE modeling.
  • You have proven your proficiency in English language equivalent to B2 level.
  • You did not reside or carry out your main activity (work, studies, etc.) in the host institution’s country for
    more than 12 months in the three years before 1st of January 2025.
  • You are ambitious, well organized, a team player, and have excellent communication skills.
  • You can work independently and have a critical mindset.
  • You are a pro-active and motivated person, eager to participate in network-wide training events, international
    mobility, and public dissemination activities.
  • Great importance will be placed on the applicants’ ability of working independently, communicating in English
    and working in group with other researchers.

6. Application requirements:

  • Curriculum vitae.
  • Motivation Letter, including a clear indication of the preferred DC position(s) within InSilicoHealth Doctoral Network if the applicant postulates for multiple positions.
  • Academic records (grades) for Bachelor and Master degrees.