Postdoc in Machine Learning, Biomedical Signal Processing, and Parallel Computing for Saving High-Risk Patients

Postdoc in Machine Learning, Biomedical Signal Processing, and Parallel Computing for Saving High-Risk Patients

Technical University of Denmark (DTU)

2800 Kgs. Lyngby, Denmark

Are you interested in contributing to save risk patients from life threatening complications using advanced methods in machine learning, biomedical signal processing, and parallel computing?

DTU Health Tech invites applicants for a 3-years Postdoc as part of a Grand Solutions Innovation Foundation research project WARD (Wireless Assessment of Respiratory and circulatory Distress), aiming at designing and implementing a new fully automatic clinical support system for all major university hospitals in Denmark.

The WARD research project team will define and develop next generation monitoring and interpretation clinical support system for highly reducing serious complications for high-risk patients.

Several miniature devices on each patient will measure the most important physiological events, and there is a need for inventing biomedical signal processing of modalities and advanced machine learning for robust interpretation of unimodal abnormal events and multimodal characterized abnormal events. Focus on real-time execution and parallel processing of data from many patients is required. Efficient interdisciplinary research between technical and medical experts is the key to success, because the fully automatic clinical support system can only be realized based on intelligent machine learning modelling of uniquely defined medical modelling and annotation of critical events.

Responsibilities and tasks
The successful candidate will be responsible for:

  • Design and implementation of machine learning algorithms to discover critical events in the measured modalities
  • Inventing new biomedical signal processing algorithms and advanced machine learning for real-time interpretation of critical events in signals from body worn “wear-and-forget” devices
  • Designing and implementing real-time parallel processing and automatic interpretation of multimodal signals and critical events from patients
  • Highly interdisciplinary collaboration between engineers, medical doctors technical PhDs and medical PhDs on development and evaluation of the clinical support system
  • Clinical support system design, implementation and evaluation
  • Contributing to efficient interdisciplinary research team collaboration
  • Writing scientific journal papers

Qualifications elaborated

  • PhD in biomedical engineering or electrical engineering, biomedical data science or equivalent qualification with preferably publication record
  • Excellent skills in advanced biomedical signal processing methods
  • Excellent skills in advanced machine learning methods
  • Very strong programming experience in Matlab, C++, and Python
  • Familiarity with high performance computing and computing clusters
  • Excellent collaborative skills, especially with research management team
  • Excellent command of English (written and spoken) as well as technical writing.
  • An understanding of advanced mathematical & statistical principles behind current best practices in high-throughput data analysis.
  • Ability to provide advice to lab members on appropriate data analysis approaches.
  • Ability to work both independently and collaboratively in complex organizations (technical/medical), and to handle several concurrent projects.
  • Exceptionally strong communication and interpersonal skills.
  • Excellent data presentation and visualization skills.
  • Ability to effectively present complex results in a clear and concise manner that is accessible to a diverse audience.
  • Enthusiasm for continued education.

Candidates should have a PhD degree in biomedical engineering or electrical engineering, biomedical data science or equivalent qualification with preferably publication record.

The assessment of the applicants will be made by the research management team: Assoc. Professor Helge B.D. Sørensen DTU Health Tech/chairman, Assoc. Professor Eske K. Aasvang, Rigshospitalet, University of Copenhagen, and Assoc. Professor Christian S. Meyhoff, Bispebjerg-Frederiksberg University Hospital, University of Copenhagen.

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.

Workplace: Department of Health Technology, Technical University of Denmark (main site) and Rigshospitalet and Bispebjerg-Frederiksberg Hospital.

You can read more about career paths at DTU here.

Further information
Further information may be obtained from Assoc. Professor Ph.D. Helge B.D. Sørensen, Digital Health, DTU Health Tech.

You can read more about DTU Health Tech on

Application procedure
Please submit your online application no later than 25 April 2019 (24:00 hours local time).

Apply online at

Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link “Apply online”, fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

  • Application (cover letter)
  • CV
  • Diploma (MSc/PhD)
  • List of publications

Applications and enclosures received after the deadline will not be considered.

All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply.

DTU Health Tech engages in research, education, and innovation base on technical and natural science for the healthcare sector. The Healthcare sector is a globally expanding market with demands for the most advanced technological solutions. DTU Health Tech creates the foundation for companies to develop new and innovative services and products which benefit people and create value for society. DTU Health Techs expertise spans from imaging and biosensor techniques, across digital health and biological modelling, to biopharma technologies.

DTU is a technical university providing internationally leading research, education, innovation and scientific advice. Our staff of 6,000 advance science and technology to create innovative solutions that meet the demands of society, and our 11,200 students are being educated to address the technological challenges of the future. DTU is an independent academic university collaborating globally with business, industry, government and public agencies.

Please apply via recruiter’s website.