Postdoc Data-driven prediction of human body motion in automated vehicles

Research / Academic

The potential discomfort and motion sickness experienced by passengers during automated journeys pose a significant challenge to their widespread adoption. Envisaged automated vehicle (AV) designs and their driving behavior are expected to provoke motion sickness and discomfort, hindering passengers' ability to enjoy their commute time. Despite substantial investments in AV technology, the importance of motion comfort has been largely overlooked. Fundamental questions regarding occupants' postural control (when and how they are activated) in AVs remain unanswered, which has led to the lack of human body models (HBM) able to predict human motion and postural control (both anticipatory as preparation for upcoming perturbation, and compensatory for restoring balance after perturbation).
To model human motion, researchers have employed simplified efficient models, which are faster to run than complex human body models and useful for early-stage design evaluations. However, these simplified models may fail to capture the intricacies of the human body's response to motion. Advanced active human body models offer highly detailed information but require specialized expertise and significant computational time. Furthermore, neither type of model adequately captures occupants' anticipatory and compensatory postural control based on upcoming or experienced motion.
To that end, this project will explore:

  1. How data driven control techniques can be employed to gain insight in occupants’ postural adjustments while being driven?
  2. To what extend can we employ feedforward and feedback components to capture anticipation and compensation?

For this, we will leverage extensive data from motion capture systems, wearable devices, and other sources from a groundbreaking experiment and we will apply nonlinear learning control techniques to model the complexities of human movement and predict occupants’ postural control while being driven.
These insights and models will enable the design of ergonomic solutions, optimization of occupant-vehicle interaction and significantly improved motion comfort in AVs. Only then, automated journeys will not only be safe and sustainable but also comfortable and enjoyable for all passengers


The candidate shall hold a:

  • PhD in Mechanical Engineering, Biomechanics, Systems and Control, or any comparable studies by the start date of the position.
  • Strong scientific programming skills
  • Strong written and oral communication skills in English

The following aspects will help you stand out:

  • Knowledge of biomechanical modelling, system identification, machine learning, control theory.
  • Prior experimental experience on human body dynamics and motion comfort.
  • A strong academit track record with publications in the relevant topics
  • The ability to act independently as well as to collaborate effectively with members of a larger team

Keep in mind that this describes the background we imagine would best fit the role. Even if you do not meet all of the requirements and feel that you are up for the task, we absolutely want to see your application!

Salary Benefits:

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities (salary indication: € 4.036 - € 5.090  per month gross). The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.
For international applicants, TU Delft has the Coming to Delft Service. This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.
This postdoc position has a fixed-term contract of 12 months.

Work Hours:

32 - 40 hours per week


Mekelweg 2