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PhD scholarship in Design of Interpretable end-to-end Deep Learning Models for Diagnosis of Sleep Disorders and Sleep Quality Evaluation

The Department of Electrical Engineering invites applicants for a 3-year PhD in sleep analytics designing biomedical signal processing and machine learning for nocturnal polysomnography (PSG) signals and other data. Half of the 3 years will be spent at DTU and Rigshospitalet, and half at Stanford University in California, USA. The PhD is part of a longstanding collaboration between these Universities for this topic. Professors with experience in technical science and sleep medicine will supervise closely the student in both locations.

In this position, you can expect to perform data analysis on tens of thousands of laboratory recorded sleep studies, in combination with genetic, proteomic and medical/questionnaire data. A sleep study, or polysomnography (PSG), is comprised of multiple physiological signals (electroencephalography, electrocardiography, electrooculography, chin and leg electromyography, respiratory, oxygen saturation, etc.) recorded simultaneously throughout the night to provide physiological measures of human activity and behavior during sleep. 

You will be expected to transform and analyze these digital signals by innovating biomedical signal processing methods as well as innovative machine learning and deep learning techniques (e.g. convolutional and recurrent neural networks), together with other signals and data.
 
Responsibilities and tasks
The purpose of the PhD project is to discover fully data-driven methods to estimate sleep quality based on biomedical analysis and automatic modelling of large cohorts of PSG and genetic data. In the process, new data-driven patterns of physiological activity during sleep will be discovered and compared to known patterns (micro-arousals, sleep stages, sleep apnea, periodic leg movements, etc.) defined by medical standards. The data processing pipeline aims at being able to evaluate sleep quality by estimating known measures of sleep quality and daytime sleepiness as well as sleep disorders such as insomnia and restless leg syndrome. The PhD student will work in a highly collaborative environment and develop novel algorithms for processing PSG and genetic data based on advanced interpretable machine learning techniques to describe and evaluate sleep quality. Collaborations across other labs and across departments are encouraged.

Experience in advanced biomedical signal processing, 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

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 2019 (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.