At present, during pregnancy fetal well-being is mainly monitored clinically through fetal heart rate and uterine contraction monitoring, in combination with ultrasound scans (e.g. 20-week echo). A combined risk analysis based on these continuous complex signals and medical record data may permit strongly improved early identification of compromised fetuses. To this end you will combine the strengths of two worlds: mathematical cardiorespiratory (physics/physiology-driven) models of the fetus-placenta-mother system to understand this physiology, combined with data-driven models of large data sets in a compound multi-parametric risk-identification model for pattern recognition. An early risk stratification allows timely intervention and may thus aid in the prevention of severe complications during pregnancy and childbirth. The proposed approach of your research includes: 1) Retrospective study to investigate clinical personal data (Máxima MC) to develop multi-parametric predictive models and identify early elevated risks for early prediction of a.o. pregnancy-induced hypertension. 2) Develop continuous risk stratification using dynamic adaptive models throughout pregnancy with improved sensitivity of risk stratification. 3) Feasibility testing of the developed hybrid model in a (prospective) pilot study to create the basis for defining unobtrusive and/or home-based monitoring strategies for high-risk pregnancies. 4) Define strategies to provide integrated/connected care platform with improved user or patient engagement that enables participation in preventive (behavioral and/or clinical) care path for those at risk of developing key pregnancy complications.

You will contribute to the PICASSO project of Eindhoven MedTech Innovation Center (e/MTIC), in collaboration with Máxima MC and Philips Research and will work in a team of engineers and clinicians at all partner locations in Eindhoven/Veldhoven.

• A master's degree (or an equivalent university degree) in (Bio)medical Engineering, Mathematics, Knowledge engineering, Physics or Electrical Engineering.
• Deep knowledge in mathematical modelling, signal processing, stochastic models.
• Some experience with machine-learning algorithms and/or parameter optimization could be a pre.
• An academic attitude and strong analytical mind.
• Enjoy working in a multidisciplinary team with academic, clinical and industrial partners.
• Fluent in spoken and written English.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation after one year.
• To support you during your PhD and to prepare you for the rest of your career, you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.