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PhD; Sleep monitoring; a multimodal approach for better diagnostic outcomes

PhD; Sleep monitoring; a multimodal approach for better diagnostic outcomes

Faculteit Electrical Engineering


PICASSO project; Long-term, at home sleep monitoring; a multimodal approach for better diagnostic outcomes

Academic environment

Eindhoven University of Technology (TU/e) is one of Europe’s top technological universities, situated in the heart of one of Europe’s largest high-tech innovation ecosystems. Research at TU/e is characterized by a combination of academic excellence and a strong real-world impact. This impact is often obtained via close collaboration with high-tech industries and key clinical centers.

In the Healthcare field, TU/e is part of a large collaborative multidisciplinary research program called the Eindhoven MedTech Innovation Center (e/MTIC). e/MTIC focuses in part on improved diagnosis and treatment of sleep disorders, with the Kempenhaeghe Center for Sleep Medicine serving as the primary clinical partner. For this project we are looking for a PhD candidate who will be appointed at TU/e, but also embedded for a part of his/her time at Kempenhaeghe and Philips Research, both located in the direct vicinity of TU/e.

Research focus

There remain a large number of undiagnosed people suffering from clinically relevant sleep disorders, resulting in a huge health and economic impact. Current diagnostic procedures for sleep disorders are too expensive and have too low specificity to allow population screening. Moreover, the current gold-standard of in-laboratory polysomnography is limited to 1 or 2 nights, failing to capture the night-to-night variability in the expression of sleep disorders. Diagnostic approaches also do not use the potential for long term assessment of subjective sleep complaints.

Recent developments in sensor and analysis technologies now enable simplified measurements of physiological characteristics at home during longer time periods. These include cardiorespiratory based methods for sleep staging, for example with wearable reflective photoplethysmography, ballistocardiographic measurements with bed sensors or body-mounted accelerometers, and simplified EEG setups using dry-electrodes which can be easily and reliably set up by the patient at home. Furthermore, app-technology makes it possible to easily log subjective sleep habits and complaints as well. In recent years, we have made important contributions to the development and initial validation of these technologies.

This now opens up the possibility to perform longitudinal measurements over several nights without negatively affecting the sleep quality of the user. We hypothesize that this will enable more precise diagnosis of sleep disorders, their subtypes and potential co-morbidities, with the objective to enable clinicians to make better treatment recommendations.


Key scientific aims of this PhD project are:

  • Literature research in the topic of wearable sleep technologies and sleep disorders
  • To investigate the clinical value that can be obtained by using advanced multimodal longitudinal home sleep measurements using a setup developed at Philips Research as compared to the current clinical practice of single-night measurements. 
  • Setting up and executing prospective studies with a multimodal setup for use in home environment, with aim of:  
    • Validating the method (on healthy individuals).
    • Assessment of the screening and diagnostic value for common sleep disorders such as sleep apnea and insomnia (in patients with suspected sleep disorders)
    • Investigation of the additional diagnostic value over current technologies, including subtyping, prediction of treatment response and phenotyping with regard to co-morbidities (in patients with suspected sleep disorders)
  • Data analytics and algorithm development for assisting diagnoses and treatment response prediction
  • Development of home triaging method for GP’s / Clinicians.


We are looking for candidates who:

  • Have a strong MSc degree (or planned to obtain MSc in the near future) in Medical, Health or Life science or related relevant fields (e.g. Biomedical Signal Processing, Cognitive Neuroscience, Sleep Medicine);
  • Have experience in conducting biomedical human experiments in clinical and home settings using physiological parameters. Knowledge of polysomnography signal processing and analysis is a pre;
  • Have strong and demonstrable background in methodology and experienced with physiological signal analysis, data processing and statistical methods (machine learning or artificial intelligence knowledge is a pre);
  • Can think out of the box, distinguish main lines from details, and provide structure to their work;
  • Have excellent multidisciplinary team working and communication skills, research & clinical professional as well as patient-facing;
  • Written fluency in English, for internal reporting and external publications;
  • Spoken fluency in Dutch and English.


  • A challenging job for 4 years in a dynamic and ambitious university and a stimulating research environment;
  • Support with your professional and personal development;
  • A gross salary per month of € 2325,- (first year) as a PhD up to € 2972,- (final year) in accordance with the Collective Labor Agreement of the Dutch Universities
  • Plus 8% holiday allowance + 8.3% end of the year allowance.
  • International PhDs and postdocs can apply for a tax reduction.   
  • An extensive package of fringe benefits, e.g., support in moving expenses, excellent technical infrastructure, on-campus child care, and excellent sports facilities.

Informatie en sollicitatie


For more information about the advertised position, please contact dr. Merel van Gilst, M.M.v.Gilst[at],More information on employment conditions can be found here: .


If interested, please use the 'apply now'-button at the top of this page. You should upload the following:

- a cover letter explaining your motivation and suitability for the position;
- a detailed Curriculum Vitae;
- a written scientific report in English (MSc thesis, traineeship report or scientific paper)
- copies of diplomas with course grades (transcripts).

You can upload only one document (maximum file size is 10 MB). You have to bundle the documents.