TU Eindhoven

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Originele vacaturetekst

PhD on Sweat analytics for semi-continuous sepsis monitoring

Project description

Sweat is a biofluid accessible non-obtrusively. It contains physiologically and metabolically rich information that can possibly be used for semi-continuous monitoring of bio-markers from patients in hospitals. Different clinical use-cases, such as dialytic patients or diabetes monitoring, could take advantage of sweat monitoring. Among them, sepsis is a life-threatening condition caused by an inflammatory immune response triggered by an infection. It is a major complication after surgery, occurring in approximately 10-15% of patients, with an associated mortality of about 38%. Early identification of sepsis can significantly reduce the risk to develop a severe condition. Non-obtrusive and prolonged sensing of sepsis-specific biomarker through sweat would be an ideal solution to monitor sepsis.

Up to now, sweat monitoring has been hampered by different technical challenges such as sweat sampling, transporting, sensing, and clinical interpretation of the results. Current advances in microfluidic devices is leading to the development of wearable devices able to decrease the sample volume and accommodate low sweat rates, enabling transporting and sensing of sweat. Nevertheless, the relationship between blood biomarkers and sweat biomarkers is debated and is affected by confounding mechanisms that complicate the clinical interpretation. Various clinically relevant sweat components are resorbed before exiting the skin. An important sepsis-related biomarker, namely lactate, is also produced by the sweat glands themselves. Attempts to account for these effects have not been effective, up to date.

To unravel the ambiguous correlation between the lactate concentrations in blood and sweat, it is crucial to consider the sweat production mechanism of the sweat glands. In fact, the concentration of several clinically relevant biomarkers not only depends on a disorder but also on the sweat rate per gland. The sweat rate per gland is determinant to differentiate between lactate originating from blood and lactate produced by the sweat glands themselves. Complex data patterns emerge from sweat measurements, requiring the development of novel data analytics strategies.

The aim of the PhD project is to develop and validate advanced mathematical models that can take into account the whole measurement chain, from bio-physiological insights to sweat transport and sensing. This model will be primarily used to guide the design of the collection device. Furthermore, to translate the complex data patterns from sensors into the desired clinically relevant results, a data analytics strategy based on such models will be designed. This will enable to estimate the sweat rate per gland and to separate artifacts from the signal.

Academic and Research 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.

Research related to this position will be carried out at the Signal Processing Systems (SPS) group in the Biomedical Diagnostic (BM/d). The BM/d lab, chaired by Prof. Mischi, has a strong track record in physiological modeling and quantitative analysis of biosignals, ranging from ultrasound and MRI to electrophysiology. For more information, see: https://www.tue.nl/en/research/research-groups/biomedical-diagnostics-lab/

The candidate will have the opportunity to work with various members of the SPS group and will be directly collaborating with Philips research within the project. The SEDAS project team also includes partners from Mechanical Engineering Department (Microsystem group), Catharina Hospital and others.


We are looking for candidates that match the following profile:

  • A master's degree in Electrical Engineering/Biomedical Engineering or related disciplines with excellent grades.
  • Excellent knowledge of signal processing and systems.
  • Solid programming skills (e.g., in Matlab and C, C++ or Labview).
  • A team player that enjoys to work in multicultural teams.
  • A research-oriented attitude.
  • Good communication and organization skills.
  • Excellent English language skills (writing and presenting).
  • Experience in physiological model development and validation is appreciated
  • A meaningful job in a dynamic and ambitious university and in a research group with an excellent reputation.
  • 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 (PROOF program).
  • 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. Assistance for finding accommodation is offered.
  • Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.
Informatie en sollicitatie

More information

If you recognize yourself in this profile and would you like to know more about the project and for any informal enquiries, please contact prof. M. Mischi (m.mischi[at]tue.nl) or dr. E. Peri (e.peri[at]tue.nl).

For information about terms of employment, click here or contact HR (hrservices.flux[at]tue.nl).

Please visit www.tue.nl/jobs to find out more about working at TU/e!


We invite you to submit a complete application by using the 'apply now'-button on this page.
The application should include:

  • a letter of motivation and portfolio with relevant work;
  • a cover letter explaining your motivation and suitability for the position;
  • a detailed Curriculum Vitae (including a list of publications and key achievements in research project(s));
  • contact information of two references;
  • copies of diplomas with course grades;
  • brief description of your MSc thesis.

Candidates will be selected based on graduation mark and proficiency at university including consideration of the reputation of the university, relevant experience and skills, writing skills and publications, work experience as well as performance in interviews.

We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.

Please keep in mind; you can upload only 5 documents up to 2 MB each.

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