1. Are you inspired by working in a multidisciplinary and international environment and by collaborating with industrial, academic, and clinical partners?
2. Are you passionate about the application of engineering methods and mathematical modeling to improve healthcare?
3. Are you eager to work on model-based solutions to accelerate the development, validation, and application of medical devices and procedures, or in silico trials in general?

Then one of the four available PhD positions could suit you perfectly well.

Job Description

The Cardiovascular Biomechanics group of the Eindhoven University of Technology, Dept.
of Biomedical Engineering, has obtained two closely related EU H2020-DTH grants for the development and validation of state-of-the-art in silico models that facilitate the design, development, and validation of medical devices and procedures.

Design, development, evaluation and clinical introduction of medical devices and procedures (including medication) is a long and costly process, soaked with all kinds of regulations regarding safety, efficacy, and the associated validation steps. Clinical acceptance of a medical device or procedure is only obtained when results of randomised clinical trials have demonstrated significant improvements in (or at least non-inferior) clinical outcomes with respect to the current gold standard. However, during current processes of device development, a lot of efforts and financial investments are still unnecessarily lost when a new device or procedure is disapproved at the clinical trial level (level III evidence).

In recent years, we have developed physiology-based (mechanistic) computational models, in close collaboration with our clinical partners at MUMC and the Catharina hospital. These models can be used for the design and evaluation of medical devices, as well as for predicting outcome of medical intervention for individual patients. These models are highly suited for virtual patient cohort generation. Such cohorts can be used to (partly) replace animals or humans during the evaluation of device safety, efficacy, and usability, and, moreover, these cohorts can overcome current limitations of real preclinical and clinical trials.

Elimination of current limitations could increase device testing success rate which makes it more interesting for companies to invest in new devices, even for rare diseases or limited user groups (e.g., children). However, creating proper virtual populations that accurately mimic real populations is not a trivial issue, and requires the development of proper validation methods and tools. These tools should not only demonstrate similarity of virtual cohorts to real patient populations, but also demonstrate robust evidence for safety, efficacy, and usability of new devices and procedures to gain the trust of the regulatory authorities and end users
(i.e., healthcare professionals, patients).

Two PhD students will contribute to the In Silico World project in collaboration with amongst others the University of Amsterdam, Erasmus University and Bologna University. The objectives of this project primarily focus on data collection, data quality assessment and certification, and synthetic data generation using physics-based, data-driven, and hybrid models. The in silico applications are covering a different applications ranging from cardiovascular (e.g. fractional flow reserve for coronary artery disease) to pharmacological applications (e.g. Multiple Sclerosis patients).

The other two PhD students will contribute to the SimCor project in collaboration with amongst others the Charite University Hospital Berlin, University College London, Biotronik, Philips Research and the Dept. of Mechanical Engineering of our own university. The objectives of this project focus on the development and validation of virtual patient cohorts using physics-based models, clinical data, augmented by synthetic (machine-learning-based) data. The cohorts will be evaluated for the design and development of two cardiovascular devices, i.e.: Transcatheter Aortic Valve Implantation (TAVI) devices for aortic valve stenosis patients, and Pulmonary Artery Pressure Sensors (PAPS) devices for heart failure patients.

The four PhD students will largely interact and should complement each other to augment the research outcomes. Also part-time secondments to our research partners are encouraged and foreseen.

• A master's degree (or an equivalent university degree) in (Bio)medical Engineering, Mathematics, Knowledge engineering, Physics or Mechanical Engineering.
• Deep knowledge in physics-based mathematical modelling.
• Some experience with machine-learning algorithms, uncertainty and sensitivity analysis, 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 and to have international collaborations.
• 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 (professional-development).
• 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.