PhD Optimizing patient transition indications through multiparametric sensing

Are you passionate about developing AI for healthcare? Interested in wearable technology that predicts patient recovery and deterioration? Join us to design a multi-parameter monitoring system, integrate AI into clinical workflows, and evaluate its impact on patient care and staff efficiency in hospitals.

Demographic changes induce an ever-higher burden on the healthcare system. Early detection of complications and diseases and the determination of effective personalized treatments are essential to lower healthcare costs while simultaneously improving patient outcome and quality of life.

To address these challenges, we are seeking a motivated candidate for a PhD position focused on the development and evaluation of an automated multi-parameter monitoring system for ambulatory patients. This role aims to support the optimization of patient transition decisions between different levels of care, while improving staff workflow efficiency.

Key responsibilities include developing a wearable system prototype to (semi-)continuously collect vital signs (e.g., ECG, HR, SpO2, RR, NiBP) and activity data (e.g., acceleration, barometer), deploying it in various hospital settings, and utilizing the collected data to build AI models that predict patient recovery, deterioration, and discharge readiness. The candidate will also investigate the use of activity data to reduce false alarms and address challenges such as alarm fatigue, a barrier to AI adoption in patient care. Additionally, the role involves integrating AI predictions into existing clinical workflows and evaluating the impact of this technology on critical parameters such as hospital length of stay, complication rates, and rehospitalization rates.

The PhD trajectory is part of the 'Medical Innovation and Research Advancing Clinical Learning and Excellence (MIRACLE)' project, a large research effort from the Eindhoven MedTech Innovation Center (e/MTIC), including 11 parallel projects. The e/MTIC combines an academic partner (TU Eindhoven) with an industrial partner (Philips) and 3 semi-academic hospitals: Máxima Medical Center, Catharina Hospital, and Kempenhaeghe. In particular, this position will be embedded in the Biomedical Diagnostics lab (Signal processing Systems group, Electrical Engineering, TU/e) in close collaboration with the Catharina Hospital Eindhoven and Philips. As a result, temporary relocation at the partners' sides (Catharina Hospital and Philips) will be considered to facilitate the project progress in its different phases.

Requirements:

• A master's degree (or an equivalent university degree) in Electrical Engineering, Biomedical Engineering, Applied Phyisics or related fields.
• Strong background in signal processing, data analysis, and machine learning.
• Proficiency in programming languages such as MATLAB and Python.
• A research-oriented attitude, analytical thinking and problem-solving skills.
• Ability to work in an interdisciplinary team and interested in collaborating with industrial and clinical partners.
• Strong interest in healthcare applications and solving clinical problems
• Fluent in spoken and written English (C1 level).

Salary Benefits:

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 evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• 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. €2,872 max. €3,670).
• 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.

38 hours per week

De Rondom 70