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PhD scholarship in Predicting Sleep Disordered Breathing from 3D Craniofacial Imaging and Polysomnography Signals

The Department of Electrical Engineering invites applicants for a 3-year interdisciplinary PhD scholarship within the fields of biomedical engineering and sleep science. The candidate will design advanced digital signal processing and machine learning techniques on nocturnal polysomnography (PSG) signals and imaging data. A PSG is a sleep study, which is comprised of multiple digital signals measured during night, such as electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), electrooculography (EOG), and several respiratory signals. Additionally, a 3D craniofacial image of the subject is taken prior to the PSG, as the anatomical features of the face in many cases are directly related to sleep disordered breathing.
 
In this position, the candidate can expect to perform data analysis on tens of thousands of recorded sleep studies. The PhD will be performed in collaboration with The Stanford Center for Sleep Sciences and Medicine at Stanford University, who are currently in the middle of their STAGES research program, which will see PSGs collected from 30.000 subjects in total as part of a large-scale ambition to study sleep and thereby improve the detection and treatment of sleep disorders. The dataset will be a crucial part of the PhD project and will provide the candidate with an opportunity to uncover important findings and be able to validate these on large-scale data.

Half of the 3 years will be spent at DTU and Rigshospitalet, while the other half will be spent at Stanford University. The PhD is part of a longstanding collaboration between DTU and Stanford and professors with experience in technical science and sleep medicine will supervise the student closely at both locations.
 
Responsibilities and tasks
The purpose of the PhD project will be to investigate the possibility of relating 3D craniofacial images to sleep disordered breathing, including whether it is possible to diagnose sleep disordered breathing and if the degree of abnormal breathing can be derived based on this imaging technology. It will be expected that the student is able to invent a fully automatic system for this purpose, using advanced machine learning techniques such as convolutional neural networks for image analysis and long-short term memory neural networks for time series analysis. Furthermore, the student will need to acquire highly specialized knowledge about the pathology of sleep disorders, such as sleep apnea, in order to define intelligent features that sufficiently describe the relationship between the anatomical structure of the face and neck and sleep disordered breathing. To carry out these investigations, the student will have data from approximately 30.000 subjects who have undergone a sleep study.
The PhD student will work in a highly collaborative environment, carry out data analysis and integration, and apply state-of-the-art algorithms - and develop new algorithms - that directly address the motivating biological questions.

Experience in advanced biomedical signal processing, machine learning, programming, advanced mathematics, and a solid background in statistical analysis are needed. Excellent interpersonal skills and the ability to interact effectively with members of the research teams are essential to the success of the individual in this position. The successful candidate must be able to learn and work independently, yet collaborate effectively with co-workers.
 
Qualifications
Candidates should have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree.

  • MSc in biomedical/electrical engineering, biomedical data science or equivalent qualification with preferably publication record
  • Excellent collaborative skills
  • Strong knowledge and experience of advanced biomedical signal processing methods and algorithms
  • 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.
  • Strong experience in the use of a high-level programming language such as MATLAB, C, C++, r for complex signal/data analysis.
  • Preferably familiarity with high performance computing and computing clusters.
  • Ability and willingness to mentor students.
  • 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.
  • Understanding of biological principles and genetics is a plus.
  • Enthusiasm for learning more.

 
Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see the DTU PhD Guide.
 
Assessment
The assessment of the applicants will be made by Assoc. Professor Helge B.D. Sørensen (DTU)/chairman, Professor Poul Jennum (Rigshospitalet CPH University), and Professor Emmanuel Mignot (Stanford University).
 
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
Biomedical Engineering, Department of Electrical Engineering, Technical University of Denmark, and Stanford University, USA.

The candidate will be enrolled as a PhD student at DTU but is required to spend up to 50% of the time at Stanford University outside Denmark.
 
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, hbds@dtu.dk Biomedical Engineering, Department of Electrical Engineering.
 
You can read more about the Department of Electrical Engineering on www.elektro.dtu.dk
 
Application
Please submit your online application no later than 15 January 2018 (local time). Apply online via the "Ansøg" link.
 
To apply, please open the link "Apply online", fill out the online application form.

The following must be attached in English: 

  • A letter motivating the application (cover letter)
  • Curriculum vitae 
  • Grade transcripts and BSc/MSc diploma
  • Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here)  

Candidates may apply prior to obtaining their master's degree but cannot begin before having received it.
 
Applications and enclosures received after the deadline will not be considered.

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

Please write in your application that you've seen the job at Jobfinder.