This PhD position at Eindhoven University of Technology offers an exciting opportunity to join the “SmartNudges” project, a collaborative initiative with Philips and Catharina Ziekenhuis (CZE) focused on advancing patient monitoring in clinical environments. The research centers on developing smart, context-aware algorithms for triggering non-invasive blood pressure (NIBP) measurements at critical moments, utilizing physiological sensor signals such as ECG and PPG. The project aims to create adaptive monitoring solutions that are both generalizable and explainable, leveraging real and synthetic data for algorithm development and exploring optimum sampling strategies, including sparse and compressed sensing techniques.

As a PhD candidate, you will design and implement a technology demonstrator prototype suitable for clinical testing, collaborate with interdisciplinary teams of clinicians, engineers, and AI researchers, and contribute to high-impact publications and international conferences. The expected outcomes include a partially explainable AI model for NIBP measurement triggering and a validated prototype for clinical use, advancing the scientific understanding of ML/AI-based adaptive monitoring in healthcare.

The position is based in the Department of Electrical Engineering, which is renowned for its research in energy conversion, telecommunication, and electrical signal processing, and maintains strong ties with high-tech industry partners. The university provides a dynamic, international, and interdisciplinary environment, with excellent technical infrastructure, training programs, and support for personal and professional growth.

Employment conditions include a full-time four-year contract, competitive salary (scale P: €3,059–€3,881/month), year-end bonus, vacation pay, pension scheme, paid parental leave, commuting and internet allowances, and dedicated support for international staff, including a tax compensation scheme. You will also participate in teaching activities (10–15% of your time) and benefit from on-campus facilities such as childcare and sports.

Applicants should hold a Master’s degree in Biomedical Engineering, Electrical Engineering, Computer Science, or a related field, with strong skills in biomedical signal processing, clinical data analysis, machine learning, and programming (Python, TensorFlow, PyTorch, Scikit-learn). Excellent English communication skills are required. Preferred qualifications include experience with wearable sensor data, multimodal datasets, and interdisciplinary teamwork.

To apply, submit your application online via the provided link, including a cover letter, CV with publications, and contact details for three references. Only complete applications will be considered. The position will remain open until filled, with a final deadline of January 18, 2026.