PhD on Development of Smart Algorithms for Context-Aware Patient Monitoring

We are seeking a highly motivated and talented PhD candidate to join the “SmartNudges” project, a cutting-edge initiative aimed at revolutionizing patient monitoring in clinical settings. The focus of this PhD research is to develop optimal methods for capturing and sampling patient states, with a primary emphasis on designing smart algorithms to trigger non-invasive blood pressure (NIBP) measurements at critical times. This will involve leveraging physiological sensor signals such as the electrocardiogram (ECG) and photoplethysmogram (PPG) to create context-aware, adaptive monitoring solutions. The research will be conducted in collaboration with Philips and the Catharina Ziekenhuis (CZE).

Information
Key Responsibilities:

• Develop a generalizable and explainable (gray-box) model for adaptive patient monitoring.
• Utilize a mixed approach combining real and synthetic data for algorithm development.
• Investigate and implement optimum sampling strategies, including sparse and compressed sampling techniques.
• Explore the applicability of neural networks in clinical workflows, ensuring solutions are practical and scalable.
• Design and build a technology demonstrator prototype of clinical-testing grade.
• Collaborate with interdisciplinary teams, including clinicians, engineers, and machine learning (ML) and artificial intelligence (AI) researchers.
• Publish findings in high-impact journals and present at international conferences.

Expected Outcomes:

• A partially explainable AI model for context-aware NIBP measurement triggering.
• A validated prototype ready for clinical testing.
• Contributions to the scientific understanding of ML / AI-based adaptive context-aware monitoring for triggering vital sign measurements in general.

• Master’s degree in Biomedical Engineering, Electrical Engineering, Computer Science, or a related field.
• Strong foundation in biomedical signal processing and hands-on experience with clinical data.
• Knowledge of information theory, sampling theorems, and optimum sampling strategies.
• Proficiency in machine learning, deep learning, and artificial intelligence techniques.
• Familiarity with clinical applications and workflows.
• Basic understanding of statistics.
• Programming expertise in Python, or similar languages, with experience in machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Excellent communication skills in English, both written and oral.

Preferred Skills:

• Affinity for understanding human physiology and clinical environments.
• Experience with wearable sensor data and multimodal datasets.
• Ability to work in interdisciplinary teams and adapt to dynamic research environments.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,059 - max. € 3,881).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.